JP6598422B2 - Medical information processing apparatus, system, and program - Google Patents

Description

  Embodiments described herein relate generally to a medical information processing apparatus, a system, and a program.

  Traditionally, in rehabilitation, many specialists aim to lead better lives for those with mental and physical disabilities caused by various causes such as illness, trauma, and aging, and congenital disabilities. Support that cooperated by is performed. For example, rehabilitation is supported by a number of specialists such as rehabilitation specialists, rehabilitation nurses, physical therapists, occupational therapists, speech therapists, clinical psychologists, prosthetic braces, and social workers.

  On the other hand, in recent years, development of a motion capture technique for digitally recording the movement of a person or an object has progressed. As a method of motion capture technology, for example, an optical type, a mechanical type, a magnetic type, a camera type and the like are known. For example, a camera system is known in which a marker is attached to a person, the marker is detected by a tracker such as a camera, and the movement of the person is digitally recorded by processing the detected marker. In addition, as a method that does not use markers and trackers, an infrared sensor is used to measure the distance from the sensor to a person, and the movement of the person is digitally detected by detecting various movements of the person's size and skeleton. The recording method is known. As a sensor using such a method, for example, Kinect (registered trademark) is known.

Japanese Patent Laid-Open No. 9-56697

  The problem to be solved by the present invention is to provide a medical information processing apparatus, system, and program capable of supporting diagnosis.

The medical information processing apparatus according to the embodiment includes a storage unit, an acquisition unit, a determination unit, and an output control unit. The storage unit stores disease information representing a disease, degree information representing the degree of the symptom, and disease operation information representing the movement of the subject according to the degree of the disease in association with each other. The acquisition unit acquires motion information representing the motion of the subject. The determination unit determines the disease information and the degree information of the subject by comparing the motion information with the disease motion information. The output control unit outputs diagnosis support information related to image diagnosis for performing a definitive diagnosis of the determined symptom information and degree information based on the disease information and degree information of the subject determined by the determination unit. .

FIG. 1 is a block diagram illustrating a configuration example of the medical information processing apparatus according to the first embodiment. FIG. 2A is a diagram for explaining processing of the motion information generation unit according to the first embodiment. FIG. 2B is a diagram for explaining processing of the motion information generation unit according to the first embodiment. FIG. 2C is a diagram for explaining processing of the motion information generation unit according to the first embodiment. FIG. 3 is a diagram illustrating an example of skeleton information generated by the motion information generation unit according to the first embodiment. FIG. 4A is a diagram for explaining a pathological condition of knee osteoarthritis. FIG. 4B is a diagram for explaining a pathological condition of knee osteoarthritis. FIG. 4C is a diagram for describing a pathological condition of knee osteoarthritis. FIG. 5 is a block diagram illustrating a detailed configuration example of the medical information processing apparatus according to the first embodiment. FIG. 6 is a diagram illustrating an example of operation information stored by the operation information storage unit according to the first embodiment. FIG. 7 is a diagram illustrating an example of information stored in the disease motion information storage unit according to the first embodiment. FIG. 8 is a diagram illustrating an example of information stored by the diagnosis support information storage unit according to the first embodiment. FIG. 9A is a diagram for explaining determination processing using an evaluation value according to the first embodiment. FIG. 9B is a diagram for describing determination processing using the evaluation value according to the first embodiment. FIG. 10A is a diagram for explaining determination processing using a trajectory according to the first embodiment. FIG. 10B is a diagram for describing determination processing using a trajectory according to the first embodiment. FIG. 11 is a flowchart illustrating a processing procedure performed by the medical information processing apparatus according to the first embodiment. FIG. 12 is a diagram illustrating an example of information stored in the disease motion information storage unit according to Modification 1 of the first embodiment. FIG. 13 is a block diagram illustrating a configuration example of the medical information processing system 1 according to the second embodiment. FIG. 14 is a diagram for explaining an example when applied to a service providing apparatus.

  A medical information processing apparatus, system, and program according to embodiments will be described below with reference to the drawings. The medical information processing apparatus described below may be used as a single medical information processing apparatus, or may be used by being incorporated in a system such as a medical record system or a rehabilitation department system. .

(First embodiment)
FIG. 1 is a block diagram illustrating a configuration example of the medical information processing apparatus 100 according to the first embodiment. The medical information processing apparatus 100 according to the first embodiment is an apparatus that supports rehabilitation performed in, for example, a medical institution, a home, or a workplace. Here, “rehabilitation” refers to a technique or method for improving the potential of a subject with a long treatment period, such as disability, chronic disease, geriatric illness, etc., and restoring and promoting life function and thus social function. Point to. Such techniques and methods include, for example, function training for restoring and promoting life functions and social functions. Here, examples of the functional training include walking training and joint range-of-motion training. In addition, a person who is a target of rehabilitation is referred to as a “subject”. The target person is, for example, a sick person, an injured person, an elderly person, a disabled person, or the like. In addition, when rehabilitation is performed, a person who assists the subject is referred to as “assistant”. This assistant is, for example, a medical worker such as a doctor, a physical therapist, or a nurse engaged in a medical institution, a caregiver who cares for the subject at home, a family member, a friend, or the like. Rehabilitation is also abbreviated as “rehabilitation”.

  As shown in FIG. 1, in the first embodiment, the medical information processing apparatus 100 is connected to the motion information collection unit 10.

  The motion information collection unit 10 detects the motion of a person or an object in a space where rehabilitation is performed, and collects motion information representing the motion of the person or the object. Note that the operation information will be described in detail when the processing of the operation information generation unit 14 described later is described. For example, Kinect (registered trademark) is used as the operation information collection unit 10.

  As illustrated in FIG. 1, the motion information collection unit 10 includes a color image collection unit 11, a distance image collection unit 12, a voice recognition unit 13, and a motion information generation unit 14. Note that the configuration of the operation information collection unit 10 illustrated in FIG. 1 is merely an example, and the embodiment is not limited thereto.

  The color image collection unit 11 photographs a subject such as a person or an object in a space where rehabilitation is performed, and collects color image information. For example, the color image collection unit 11 detects light reflected from the subject surface with a light receiving element, and converts visible light into an electrical signal. Then, the color image collection unit 11 converts the electrical signal into digital data, thereby generating one frame of color image information corresponding to the shooting range. The color image information for one frame includes, for example, shooting time information and information in which RGB (Red Green Blue) values are associated with each pixel included in the one frame. The color image collection unit 11 shoots a moving image of the shooting range by generating color image information of a plurality of continuous frames from visible light detected one after another. The color image information generated by the color image collection unit 11 may be output as a color image in which the RGB values of each pixel are arranged in a bitmap. Further, the color image collection unit 11 includes, for example, a complementary metal oxide semiconductor (CMOS) or a charge coupled device (CCD) as a light receiving element.

  The distance image collection unit 12 photographs a subject such as a person or an object in a space where rehabilitation is performed, and collects distance image information. For example, the distance image collection unit 12 irradiates the surrounding area with infrared rays, and detects a reflected wave obtained by reflecting the irradiation wave on the surface of the subject with the light receiving element. Then, the distance image collection unit 12 obtains the distance between the subject and the distance image collection unit 12 based on the phase difference between the irradiation wave and the reflected wave and the time from irradiation to detection, and corresponds to the shooting range. Generate frame distance image information. The distance image information for one frame includes, for example, shooting time information and information in which each pixel included in the shooting range is associated with the distance between the subject corresponding to the pixel and the distance image collection unit 12. included. The distance image collection unit 12 captures a moving image of the shooting range by generating distance image information of a plurality of continuous frames from reflected waves detected one after another. The distance image information generated by the distance image collection unit 12 may be output as a distance image in which color shades corresponding to the distance of each pixel are arranged in a bitmap. The distance image collection unit 12 includes, for example, a CMOS or a CCD as a light receiving element. This light receiving element may be shared with the light receiving element used in the color image collection unit 11. The unit of the distance calculated by the distance image collection unit 12 is, for example, meters [m].

  The voice recognition unit 13 collects surrounding voices, performs sound source direction specification and voice recognition. The voice recognition unit 13 has a microphone array including a plurality of microphones, and performs beam forming. Beam forming is a technique for selectively collecting sound from a specific direction. For example, the voice recognition unit 13 specifies the direction of the sound source by beam forming using a microphone array. The voice recognition unit 13 recognizes a word from the collected voice using a known voice recognition technique. That is, the speech recognition unit 13 generates, as a speech recognition result, for example, information associated with a word recognized by the speech recognition technology, a direction in which the word is emitted, and a time at which the word is recognized.

  The motion information generation unit 14 generates motion information representing the motion of a person or an object. This motion information is generated by, for example, capturing a human motion (gesture) as a series of a plurality of postures (poses). In brief, the motion information generation unit 14 first obtains the coordinates of each joint forming the skeleton of the human body from the distance image information generated by the distance image collection unit 12 by pattern matching using a human body pattern. The coordinates of each joint obtained from the distance image information are values represented by a distance image coordinate system (hereinafter referred to as “distance image coordinate system”). Therefore, the motion information generation unit 14 then represents the coordinates of each joint in the distance image coordinate system in a three-dimensional space coordinate system in which rehabilitation is performed (hereinafter referred to as a “world coordinate system”). Convert to The coordinates of each joint represented in this world coordinate system become the skeleton information for one frame. Further, the skeleton information for a plurality of frames is the operation information. Hereinafter, processing of the motion information generation unit 14 according to the first embodiment will be specifically described.

  2A to 2C are diagrams for explaining processing of the motion information generation unit 14 according to the first embodiment. FIG. 2A shows an example of a distance image generated by the distance image collection unit 12. In FIG. 2A, for convenience of explanation, an image expressed by a line drawing is shown. However, an actual distance image is an image expressed by shading of colors according to the distance. In this distance image, each pixel has a “pixel position X” in the left-right direction of the distance image, a “pixel position Y” in the up-down direction of the distance image, and a subject corresponding to the pixel and the distance image collection unit 12. It has a three-dimensional value associated with “distance Z”. Hereinafter, the coordinate value of the distance image coordinate system is expressed by the three-dimensional value (X, Y, Z).

  In the first embodiment, the motion information generation unit 14 stores in advance human body patterns corresponding to various postures by learning, for example. Each time the distance image collection unit 12 generates distance image information, the motion information generation unit 14 acquires the generated distance image information of each frame. Then, the motion information generation unit 14 performs pattern matching using a human body pattern on the acquired distance image information of each frame.

  Here, the human body pattern will be described. FIG. 2B shows an example of a human body pattern. In the first embodiment, since the human body pattern is a pattern used for pattern matching with distance image information, it is expressed in the distance image coordinate system, and is similar to the person depicted in the distance image, on the surface of the human body. Information (hereinafter referred to as “human body surface”). For example, the human body surface corresponds to the skin or clothing surface of the person. Further, as shown in FIG. 2B, the human body pattern includes information on each joint forming the skeleton of the human body. That is, in the human body pattern, the relative positional relationship between the human body surface and each joint is known.

  In the example illustrated in FIG. 2B, the human body pattern includes information on 20 joints from the joint 2a to the joint 2t. Of these, the joint 2a corresponds to the head, the joint 2b corresponds to the center of both shoulders, the joint 2c corresponds to the waist, and the joint 2d corresponds to the center of the buttocks. The joint 2e corresponds to the right shoulder, the joint 2f corresponds to the right elbow, the joint 2g corresponds to the right wrist, and the joint 2h corresponds to the right hand. The joint 2i corresponds to the left shoulder, the joint 2j corresponds to the left elbow, the joint 2k corresponds to the left wrist, and the joint 2l corresponds to the left hand. Also, the joint 2m corresponds to the right hip, the joint 2n corresponds to the right knee, the joint 2o corresponds to the right ankle, and the joint 2p corresponds to the right foot. Further, the joint 2q corresponds to the left hip, the joint 2r corresponds to the left knee, the joint 2s corresponds to the left ankle, and the joint 2t corresponds to the left foot.

  In FIG. 2B, the case where the human body pattern has information on 20 joints has been described. However, the embodiment is not limited to this, and the position and number of joints may be arbitrarily set by the operator. . For example, when only the change in the movement of the limbs is captured, information on the joint 2b and the joint 2c among the joints 2a to 2d may not be acquired. In addition, when capturing changes in the movement of the right hand in detail, not only the joint 2h but also the joint of the finger of the right hand may be newly set. The joint 2a, the joint 2h, the joint 2l, the joint 2p, and the joint 2t in FIG. 2B are different from so-called joints because they are the end portions of the bone, but are important points representing the position and orientation of the bone. For the sake of convenience, it is described here as a joint.

  The motion information generation unit 14 performs pattern matching with the distance image information of each frame using the human body pattern. For example, the motion information generation unit 14 extracts a person in a certain posture from the distance image information by pattern matching the human body surface of the human body pattern shown in FIG. 2B and the distance image shown in FIG. 2A. In this way, the motion information generation unit 14 obtains the coordinates of the human body surface depicted in the distance image. Further, as described above, in the human body pattern, the relative positional relationship between the human body surface and each joint is known. Therefore, the motion information generation unit 14 calculates the coordinates of each joint in the person from the coordinates of the human body surface depicted in the distance image. Thus, as illustrated in FIG. 2C, the motion information generation unit 14 acquires the coordinates of each joint forming the skeleton of the human body from the distance image information. Note that the coordinates of each joint obtained here are the coordinates of the distance coordinate system.

  In addition, the motion information generation part 14 may use the information showing the positional relationship of each joint supplementarily when performing pattern matching. The information representing the positional relationship between the joints includes, for example, a joint relationship between the joints (for example, “joint 2a and joint 2b are coupled”) and a movable range of each joint. A joint is a site that connects two or more bones. The angle between the bones changes according to the change in posture, and the range of motion differs depending on the joint. For example, the range of motion is represented by the maximum and minimum values of the angles formed by the bones connected by each joint. For example, when learning the human body pattern, the motion information generation unit 14 also learns the range of motion of each joint and stores it in association with each joint.

  Subsequently, the motion information generation unit 14 converts the coordinates of each joint in the distance image coordinate system into values represented in the world coordinate system. The world coordinate system is a coordinate system in a three-dimensional space where rehabilitation is performed. For example, the position of the motion information collection unit 10 is the origin, the horizontal direction is the x axis, the vertical direction is the y axis, and the direction is orthogonal to the xy plane. Is a coordinate system with z as the z-axis. The coordinate value in the z-axis direction may be referred to as “depth”.

  Here, the process of converting from the distance image coordinate system to the world coordinate system will be described. In the first embodiment, it is assumed that the motion information generation unit 14 stores in advance a conversion formula for converting from the distance image coordinate system to the world coordinate system. For example, this conversion formula receives the coordinates of the distance image coordinate system and the incident angle of the reflected light corresponding to the coordinates, and outputs the coordinates of the world coordinate system. For example, the motion information generation unit 14 inputs the coordinates (X1, Y1, Z1) of a certain joint and the incident angle of reflected light corresponding to the coordinates to the conversion formula, and coordinates (X1, Y1) of the certain joint , Z1) are converted into coordinates (x1, y1, z1) in the world coordinate system. Since the correspondence relationship between the coordinates of the distance image coordinate system and the incident angle of the reflected light is known, the motion information generation unit 14 inputs the incident angle corresponding to the coordinates (X1, Y1, Z1) into the conversion equation. can do. Although the case has been described here in which the motion information generation unit 14 converts the coordinates of the distance image coordinate system to the coordinates of the world coordinate system, it is also possible to convert the coordinates of the world coordinate system to the coordinates of the distance coordinate system. is there.

  Then, the motion information generation unit 14 generates skeleton information from the coordinates of each joint represented in the world coordinate system. FIG. 3 is a diagram illustrating an example of skeleton information generated by the motion information generation unit 14. The skeleton information of each frame includes shooting time information of the frame and coordinates of each joint. For example, as illustrated in FIG. 3, the motion information generation unit 14 generates skeleton information in which joint identification information and coordinate information are associated with each other. In FIG. 3, the shooting time information is not shown. The joint identification information is identification information for identifying a joint and is set in advance. For example, joint identification information “2a” corresponds to the head, and joint identification information “2b” corresponds to the center of both shoulders. Similarly for the other joint identification information, each joint identification information indicates a corresponding joint. The coordinate information indicates the coordinates of each joint in each frame in the world coordinate system.

  In the first line of FIG. 3, joint identification information “2a” and coordinate information “(x1, y1, z1)” are associated. That is, the skeleton information in FIG. 3 indicates that the head is present at the coordinates (x1, y1, z1) in a certain frame. Also, in the second row of FIG. 3, joint identification information “2b” and coordinate information “(x2, y2, z2)” are associated. That is, the skeleton information in FIG. 3 indicates that the center of both shoulders exists at the position of coordinates (x2, y2, z2) in a certain frame. Similarly, other joints indicate that each joint exists at a position represented by each coordinate in a certain frame.

  In this way, every time the distance image information of each frame is acquired from the distance image collection unit 12, the motion information generation unit 14 performs pattern matching on the distance image information of each frame, and the world coordinate from the distance image coordinate system. By converting into a system, skeleton information of each frame is generated. Then, the motion information generation unit 14 outputs the generated skeleton information of each frame to the medical information processing apparatus 100 and stores it in the later-described motion information storage unit 131.

  Note that the processing of the motion information generation unit 14 is not limited to the above-described method. For example, in the above description, the method in which the motion information generation unit 14 performs pattern matching using a human body pattern has been described, but the embodiment is not limited thereto. For example, instead of the human body pattern or together with the human body pattern, a pattern matching method using a pattern for each part may be used.

  For example, in the above description, the method in which the motion information generation unit 14 obtains the coordinates of each joint from the distance image information has been described, but the embodiment is not limited thereto. For example, the motion information generation unit 14 may obtain a coordinate of each joint using color image information together with distance image information. In this case, for example, the motion information generation unit 14 performs pattern matching between the human body pattern expressed in the color image coordinate system and the color image information, and obtains the coordinates of the human body surface from the color image information. The coordinate system of this color image does not include the “distance Z” information referred to in the distance image coordinate system. Therefore, for example, the motion information generation unit 14 obtains the information of “distance Z” from the distance image information, and obtains the coordinates of the world coordinate system of each joint by calculation processing using these two pieces of information.

  Further, the motion information generation unit 14 needs the color image information generated by the color image collection unit 11, the distance image information generated by the distance image collection unit 12, and the voice recognition result output by the voice recognition unit 13. Accordingly, the information is appropriately output to the medical information processing apparatus 100 and stored in the operation information storage unit 131 described later. The pixel position of the color image information and the pixel position of the distance image information can be associated in advance according to the positions of the color image collection unit 11 and the distance image collection unit 12 and the shooting direction. For this reason, the pixel position of the color image information and the pixel position of the distance image information can be associated with the world coordinate system calculated by the motion information generation unit 14. Furthermore, by using this correspondence and the distance [m] calculated by the distance image collection unit 12, the height and the length of each part of the body (the length of the arm and the length of the abdomen) can be obtained, or on the color image. It is possible to obtain a distance between two designated pixels. Similarly, the photographing time information of the color image information and the photographing time information of the distance image information can be associated in advance. In addition, the motion information generation unit 14 refers to the speech recognition result and the distance image information, and if there is a joint 2a in the vicinity of the direction in which the speech-recognized word is issued at a certain time, the person including the joint 2a It can be output as an emitted word. Furthermore, the motion information generation unit 14 also appropriately outputs information representing the positional relationship between the joints to the medical information processing apparatus 100 as necessary, and stores the information in the storage unit 130 described later.

  In addition, the motion information generation unit 14 generates one frame of depth image information corresponding to the imaging range, using the depth which is a coordinate value in the z-axis direction of the world coordinate system. The depth image information for one frame includes, for example, shooting time information and information in which each pixel included in the shooting range is associated with a depth corresponding to the pixel. In other words, the depth image information is obtained by associating depth information in place of the distance information associated with each pixel of the distance image information, and representing each pixel position in the same distance image coordinate system as the distance image information. be able to. The motion information generation unit 14 outputs the generated depth image information to the medical information processing apparatus 100 and stores it in the motion information storage unit 131 described later. It should be noted that the depth image information may be output as a depth image in which color shades corresponding to the depth of each pixel are arranged in a bitmap.

  Although the case where the motion information collecting unit 10 detects the motion of a single person has been described here, the embodiment is not limited to this. If it is included in the shooting range of the motion information collection unit 10, the motion information collection unit 10 may detect the motions of a plurality of persons. When a plurality of persons are photographed in the distance image information of the same frame, the motion information collection unit 10 associates the skeleton information of the plurality of persons generated from the distance image information of the same frame, This is output to the medical information processing apparatus 100 as operation information.

  Further, the configuration of the operation information collection unit 10 is not limited to the above configuration. For example, when motion information is generated by detecting the motion of a person by other motion capture, such as optical, mechanical, magnetic, etc., the motion information collection unit 10 does not necessarily include the distance image collection unit 12. It does not have to be. In such a case, the motion information collection unit 10 includes, as motion sensors, a marker that is worn on the human body in order to detect a human motion, and a sensor that detects the marker. Then, the motion information collection unit 10 detects motion of a person using a motion sensor and generates motion information. Further, the motion information collecting unit 10 associates the pixel position of the color image information with the coordinates of the motion information using the position of the marker included in the image photographed by the color image collecting unit 11, and if necessary, Output to the medical information processing apparatus 100 as appropriate. For example, the motion information collection unit 10 may not include the speech recognition unit 13 when the speech recognition result is not output to the medical information processing apparatus 100.

  Furthermore, in the embodiment described above, the motion information collection unit 10 outputs the coordinates of the world coordinate system as the skeleton information, but the embodiment is not limited to this. For example, the motion information collection unit 10 outputs the coordinates of the distance image coordinate system before conversion, and the conversion from the distance image coordinate system to the world coordinate system may be performed on the medical information processing apparatus 100 side as necessary. Good.

  Returning to the description of FIG. The medical information processing apparatus 100 performs processing for supporting rehabilitation using the motion information output from the motion information collection unit 10. The medical information processing apparatus 100 is an information processing apparatus such as a computer or a workstation, for example, and includes an output unit 110, an input unit 120, a storage unit 130, and a control unit 140, as shown in FIG.

  The output unit 110 outputs various types of information for supporting rehabilitation. For example, the output unit 110 displays a GUI (Graphical User Interface) for an operator operating the medical information processing apparatus 100 to input various requests using the input unit 120, or is generated in the medical information processing apparatus 100. The output image or the like is displayed, or a warning sound is output. For example, the output unit 110 is a monitor, a speaker, headphones, a headphone portion of a headset, or the like. Further, the output unit 110 may be a display of a system that is worn on the user's body, such as a glasses-type display or a head-mounted display.

  The input unit 120 accepts input of various information for supporting rehabilitation. For example, the input unit 120 receives input of various requests from an operator of the medical information processing apparatus 100 and transfers the received various requests to the medical information processing apparatus 100. For example, the input unit 120 is a mouse, a keyboard, a touch command screen, a trackball, a microphone, a microphone portion of a headset, or the like. The input unit 120 may be a sensor that acquires biological information such as a sphygmomanometer, a heart rate monitor, or a thermometer.

  The storage unit 130 is, for example, a semiconductor memory device such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk device or an optical disk device. The control unit 140 can be realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA) or a central processing unit (CPU) executing a predetermined program.

  The configuration of the medical information processing apparatus 100 according to the first embodiment has been described above. With this configuration, the medical information processing apparatus 100 according to the first embodiment can support diagnosis. For example, the medical information processing apparatus 100 can support a doctor's diagnosis when diagnosing a disease that is difficult to diagnose early. An example of a disease that is difficult to diagnose early is osteoarthritis of the knee. Osteoarthritis is a disease caused by degenerative (aging) changes in articular cartilage, where the articular cartilage of the knee is worn down and the nerves in the subchondral bone are exposed, and the nerve is stimulated to cause pain. It is a disease that occurs. In addition, since the worn cartilage is difficult to return to the original state, and particularly in elderly people, it is important to prevent progression of the disease state by early diagnosis of knee osteoarthritis.

  4A to 4C are diagrams for explaining a pathological condition of knee osteoarthritis. FIG. 4A illustrates the pathological condition in the early stage of knee osteoarthritis, FIG. 4B illustrates the pathological condition in the advanced stage of knee osteoarthritis, and FIG. 4C illustrates the pathological condition in the last stage of knee osteoarthritis. Illustrate the pathology.

  As shown in FIG. 4A, in the early stage of knee osteoarthritis, the knee articular cartilage is worn. Since articular cartilage itself has no nerves, symptoms (knee pain) rarely appear in this condition. When the wear of the articular cartilage proceeds from this pathological condition, a crack occurs in the articular cartilage as shown in FIG. 4B. In this pathological condition, pain often appears depending on the degree of the pathological condition. When the disease progresses further to the end stage, as shown in FIG. 4C, the articular cartilage disappears and the subchondral bone is exposed. In this condition, the subchondral bone is directly damaged and forms bone sclerosis and cysts.

  Here, conventionally, in order to diagnose the pathological condition of knee osteoarthritis, image diagnosis by X-ray examination or MRI (Magnetic Resonance Imaging) examination is performed. For example, in an image finding (X-ray finding) by X-ray inspection, a specific imaging method called a load X-ray inspection is performed in which X-ray imaging is performed in a standing posture where a load is applied to the knee. If one of the three X-ray findings of osteophyte formation, bone crest formation, bone sclerosis, and joint space narrowing is found in the X-ray image taken by this load X-ray examination, the deformable knee joint Diagnosed with symptom.

Further, for example, in the MRI examination, a method of taking a T2 ^* weighted image is used. In this T2 ^* weighted image, if one of the four MRI findings of mirror region, osteophyte formation, bone sclerosis formation, bone sclerosis and bone cyst formation is detected, it is diagnosed as osteoarthritis of the knee. The Note that the mirror region indicates a state in which the femoral joint surface and the tibial joint surface collide and damage has occurred in the bone marrow.

However, with the above diagnosis, early diagnosis of knee osteoarthritis has been difficult. As this factor, for example, in order to perform the above-described image diagnosis, it is necessary to perform imaging by a specific imaging method. In other words, when diagnosing knee pain, there are presumed diseases other than knee osteoarthritis, so a load X-ray examination and T2 ^* weighted image only for the purpose of diagnosing knee osteoarthritis This was rarely performed unless significant pain occurred. For this reason, it has been difficult to perform early diagnosis of knee osteoarthritis by conventional image diagnosis. In addition, since imaging conditions and diagnostic methods by specific methods require skilled skills, doctors and technicians with little experience may miss the next execution of appropriate imaging and diagnosis.

  Another factor is that knee pain tends to be neglected by the subject (patient). In other words, even if pain occurs in some knees, some subjects may not visit a medical institution until inconvenience occurs in daily life and may visit after inconvenience has occurred. In this case, since the pathological condition of the subject has already progressed, it may be too late to prevent the pathological condition from progressing. That is, early diagnosis of knee osteoarthritis has been difficult due to delays in medical examinations.

  Therefore, the medical information processing apparatus 100 according to the first embodiment performs the process described below, for example, even when diagnosing a disease such as knee osteoarthritis that is difficult to diagnose early, Diagnosis can be supported.

  Hereinafter, a case where the medical information processing apparatus 100 supports diagnosis of knee osteoarthritis will be described. Specifically, in the first embodiment, when a medical examination is performed on an unspecified number of subjects in a certain facility, an early diagnosis of knee osteoarthritis is performed using the medical information processing apparatus 100. Explain the case. However, the embodiment is not limited to this, and for example, it may be a case where osteoarthritis is diagnosed for a specific subject who has visited a medical institution.

  Further, for example, the medical information processing apparatus 100 is not limited to the diagnosis of osteoarthritis of the knee, and may be applied to the diagnosis of osteoarthritis of a part different from the knee. Specifically, osteoarthritis can occur in any joint in the whole body, but it can occur in the neck joint, shoulder joint, elbow joint, finger joint, spine, hip joint, knee joint and toe joint. It is easy to develop in joints. Among these, the spine, hip joint, and knee joint are easy to put on the subject's own body weight, and thus inconvenience is likely to occur in daily life. The medical information processing apparatus 100 can be applied to the diagnosis of these osteoarthritis.

  The medical information processing apparatus 100 is not limited to osteoarthritis, but fractures (pathological, fatigue), dislocation (pathological), baseball shoulders, baseball elbows, tennis elbows, fifty shoulders, cervical spondylosis, osteoporosis, disc herniation, It is widely applicable to orthopedic diseases such as bone tumors, intercostal neuralgia, and sciatica that can confirm initial symptoms. In addition to orthopedic diseases, for example, respiratory disease, digestive system disease, circulatory system disease, nervous system disease, psychiatric disease, urological disease, etc. Applicable to diseases in which changes in movement occur. In addition, for example, the medical information processing apparatus 100 can confirm changes in daily life operations indicating basic behaviors indispensable for living, such as eating, changing clothes, movement, excretion, conditioning, bathing, etc. It can be applied to any symptom. In addition, the medical information processing apparatus 100 is not limited to the diagnosis of a disease that is difficult to diagnose early, and may be applied to, for example, the diagnosis of the terminal symptoms of the disease.

  FIG. 5 is a block diagram illustrating a detailed configuration example of the medical information processing apparatus 100 according to the first embodiment. As shown in FIG. 5, in the medical information processing apparatus 100, the storage unit 130 includes an operation information storage unit 131, a disease operation information storage unit 132, and a diagnosis support information storage unit 133.

  The motion information storage unit 131 stores various information collected by the motion information collection unit 10. Specifically, the motion information storage unit 131 stores the motion information generated by the motion information generation unit 14. More specifically, the motion information storage unit 131 stores skeleton information for each frame generated by the motion information generation unit 14. Here, it is also possible to store the color image information, the distance image information, and the speech recognition result output by the motion information storage unit 131 and the motion information generation unit 14 in association with each other for each frame.

  FIG. 6 is a diagram illustrating an example of operation information stored in the operation information storage unit 131 according to the first embodiment. As illustrated in FIG. 6, the motion information storage unit 131 stores information in which a name number, an execution date, and motion information are associated with each other. Here, the “name number” is an identifier for uniquely identifying the target person, and is assigned to each name. The “implementation date” indicates the date and time when the subject performed walking training. “Operation information” indicates information collected by the operation information collection unit 10.

  As illustrated in FIG. 6, the motion information storage unit 131 includes a name “A”, a name number “1”, an implementation date “20120801_1”, motion information “color image information, distance image information, speech recognition result, skeleton information”, and the like. Remember. The above-mentioned information includes “color image information” as motion information in the “first” walking training conducted on “August 1” in “2012” by the person with the name “A” having the name number “1”. ”,“ Distance image information ”,“ speech recognition result ”, and“ skeleton information ”.

  Here, in the motion information shown in FIG. 6, “color image information”, “distance image information”, “speech recognition result”, and “skeleton information” for every frame taken when the walking training is executed. Are stored in association with time in chronological order.

  In addition, as illustrated in FIG. 6, the motion information storage unit 131 includes a name “A”, a name number “1”, an implementation date “20120801_2”, a motion information “color image information, distance image information, speech recognition result, skeleton information”. "Etc. are memorized. That is, the motion information storage unit 131 similarly stores motion information in the “second” walking training performed by the person with the name “A” on “August 1” in “2012”.

  Further, as illustrated in FIG. 6, the motion information storage unit 131 also performs “color image information”, “distance image information”, “voice recognition result”, and “ The motion information including “skeleton information” is stored. As described above, the motion information storage unit 131 stores the walking training motion information collected for each subject in association with each subject.

  Note that the information stored in the operation information storage unit 131 illustrated in FIG. 6 is merely an example, and the present invention is not limited to this. For example, the motion information storage unit 131 may further store information other than “color image information”, “distance image information”, “voice recognition result”, and “skeleton information” illustrated in FIG. Further, for example, the motion information storage unit 131 may store the motion information as not including a speech recognition result when the motion information collection unit 10 does not have the speech recognition unit 13. The “color image information” and “distance image information” included in the operation information may be bitmap (bitmap), JPEG, other binary format image data itself, a link to the image data, or the like. Further, the “voice recognition result” included in the operation information may be voice data itself, a link to the recognition information or voice data, or the like in addition to the above-described recognition information.

  The disease motion information storage unit 132 stores the disease information representing the disease and the disease motion information representing the motion of the subject having the disease in association with each other. The disease motion information storage unit 132 is referred to by the determination unit 142 described later. The disease motion information storage unit 132 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 7 is a diagram illustrating an example of information stored by the disease motion information storage unit 132 according to the first embodiment. As shown in FIG. 7, the disease motion information storage unit 132 stores information in which disease information and disease motion information are associated with each other. Here, “disease information” indicates information indicating a disease, and includes, for example, a disease name and grade. The “disease name” indicates information indicating the name of the disease. “Grade” indicates information indicating the degree of progression of the disease state. Further, “disease motion information” indicates motion information indicating the motion of a subject having a disease represented by the disease information. For example, the disease motion information indicates motion information in rehabilitation of a typical subject. As an example, the disease motion information is motion information of gait training performed by a knee osteoarthritis subject.

  Here, the grade of knee osteoarthritis will be described. The pathological condition of knee osteoarthritis is classified into six grades based on, for example, X-ray findings. That is, when “normal” is diagnosed in the X-ray findings, it is classified as grade “0”. In addition, when “bone sclerosis image or osteophyte” is seen in the X-ray findings, it is classified as grade “1”. Moreover, when “narrowing of joint space (less than 3 mm)” is observed in the X-ray findings, it is classified into grade “2”. In addition, when “joint space closure or subluxation” is found in the X-ray findings, it is classified as grade “3”. Further, when “load surface wear or chipping (less than 5 mm)” is observed in the X-ray findings, it is classified as grade “4”. Further, when “abrasion or chipping of load surface (5 mm or more)” is observed in the X-ray findings, it is classified as grade “5”. This classification is referred to as a lumbar area classification.

  As illustrated in FIG. 7, the disease motion information storage unit 132 includes a disease name “osteoarthritis of the knee”, a grade “0”, and motion information “color image information, distance image information, speech recognition result, skeletal information”. Memorize etc. That is, the disease motion information storage unit 132 stores motion information in rehabilitation (walking training) of a representative subject diagnosed with grade “0” of knee osteoarthritis. Further, the disease motion information storage unit 132 stores a disease name “osteoarthritis of the knee”, grade “1”, motion information “color image information, distance image information, speech recognition result, skeletal information”, and the like. That is, the disease motion information storage unit 132 stores motion information in rehabilitation (walking training) of a representative subject diagnosed with grade “1” of knee osteoarthritis. Further, the disease motion information storage unit 132 stores a disease name “osteoarthritis of the knee”, grade “2”, motion information “color image information, distance image information, speech recognition result, skeletal information”, and the like. That is, the disease motion information storage unit 132 stores motion information in rehabilitation (walking training) of a representative subject diagnosed with grade “2” of knee osteoarthritis. Similarly, the disease motion information storage unit 132 stores information in which the disease name, grade, and motion information are associated with each other.

  In addition, the information memorize | stored in the disease action information storage part 132 shown in FIG. 7 is an example to the last, and is not limited to this. For example, the disease motion information storage unit 132 may store a grade based on a classification different from the above-mentioned classification of the lumbar region and disease motion information corresponding to each grade. For example, the disease operation information storage unit 132 may store a plurality of disease names and disease operation information corresponding to each disease name. In addition, the disease motion information storage unit 132 stores only the disease name as the disease information and does not have to store grade. In addition, the disease motion information storage unit 132 may store a motion model created based on motion information of a plurality of subjects as the disease motion information. In addition, the disease motion information storage unit 132 may store not only a disease but also information regarding symptoms. That is, the disease motion information storage unit 132 may store the symptom information indicating the symptom and the symptom motion information indicating the motion of the subject having the symptom in association with each other. The disease information is an example of symptom information, and the disease operation information is an example of symptom operation information.

  The diagnosis support information storage unit 133 stores information for supporting diagnosis of a disease. For example, the diagnosis support information storage unit 133 is referred to by an output control unit 143 described later. The diagnosis support information storage unit 133 is registered in advance by the operator.

  FIG. 8 is a diagram illustrating an example of information stored by the diagnosis support information storage unit 133 according to the first embodiment. The diagnosis support information storage unit 133 stores information in which disease information and diagnosis support information are associated with each other as illustrated in FIG. Here, “diagnosis support information” indicates information for supporting diagnosis of a disease. For example, the diagnosis support information includes imaging related information and interpretation points. Among these, “imaging related information” indicates an imaging method and imaging conditions for imaging for image diagnosis. The “interpretation point” indicates a point when the captured image is interpreted.

  As illustrated in FIG. 8, the diagnosis support information storage unit 133 includes a disease name “osteoarthritis of the knee”, grade “0”, imaging related information “image diagnosis unnecessary”, an interpretation point “− (information is empty Is stored). ”Is stored. That is, the diagnosis support information storage unit 133 stores that image diagnosis is not necessary for the diagnosis of grade “0” of knee osteoarthritis. In this case, since image diagnosis is unnecessary, the information on the interpretation points stored in the diagnosis support information storage unit 133 is empty. In addition, the diagnosis support information storage unit 133 includes a disease name “degenerative knee joint”, grade “1”, imaging related information “X-ray examination (frontal image)”, and an interpretation point “bone sclerosis image or osteophyte”. Is associated with "Can you see?" That is, the diagnosis support information storage unit 133 is recommended to take a front image by X-ray examination for diagnosis of grade “1” of knee osteoarthritis, and whether or not an osteosclerosis image or an osteophyte is seen. Remember that is the point of diagnosis. Further, the diagnosis support information storage unit 133 includes a disease name “degenerative knee joint disease”, a grade “2”, imaging related information “load X-ray examination (front view)”, an interpretation point “a narrowness of joint space”. Information associated with “whether it can be seen (less than 3 mm)” is stored. That is, the diagnosis support information storage unit 133 is recommended to take a front image by load X-ray examination for diagnosis of grade “2” of knee osteoarthritis, and the joint space is narrowed (less than 3 mm). It is memorized that whether or not it is seen is a point of diagnosis. Similarly, the diagnosis support information storage unit 133 stores information in which disease names, grades, imaging related information, and interpretation points are associated with each other.

  Note that the information stored in the diagnosis support information storage unit 133 illustrated in FIG. 8 is merely an example, and is not limited thereto. For example, the diagnosis support information storage unit 133 may store only the disease name as the disease information and may not store grade. In addition, the diagnosis support information storage unit 133 may store any one of imaging related information and interpretation points as diagnosis support information.

  Returning to the description of FIG. As illustrated in FIG. 5, in the medical information processing apparatus 100, the control unit 140 includes an acquisition unit 141, a determination unit 142, and an output control unit 143.

  The acquisition unit 141 acquires motion information of the subject. For example, the acquisition unit 141 stores the stored skeleton information and the stored information every time the motion information collection unit 10 and the medical information processing apparatus 100 are turned on and one frame of skeleton information is stored in the motion information storage unit 131. The color image information of the frame corresponding to the skeleton information obtained is acquired from the motion information storage unit 131, respectively.

  The determination unit 142 determines the disease information of the subject by comparing the motion information of the subject and the disease motion information. For example, the determination unit 142 compares the motion information of the subject acquired by the acquisition unit 141 with the disease motion information stored in the disease motion information storage unit 132, and the disease most similar to the acquired motion information. Specify operation information. Then, the determination unit 142 determines the disease information associated with the specified disease operation information as the disease information of the subject.

  For example, the determination unit 142 uses either a determination process using an evaluation value calculated from motion information or a determination process using a predetermined joint trajectory included in the motion information, to determine the disease information of the subject. Determine. Hereinafter, these two determination processes will be described in order.

(Judgment process using evaluation value)
A determination process using the evaluation value will be described. For example, the determination unit 142 calculates the evaluation value of the subject from the motion information of the subject, and calculates the evaluation value of the disease from the disease motion information stored in the disease motion information storage unit 132. Then, the disease motion information storage unit 132 compares the calculated evaluation value of the subject with the evaluation value of the disease, and determines the disease information corresponding to the evaluation value of the disease closest to the evaluation value of the subject.

  9A and 9B are diagrams for explaining the determination process using the evaluation value according to the first embodiment. 9A and 9B illustrate a case where the determination unit 142 determines the knee osteoarthritis grade using the knee angle in walking. FIG. 9A illustrates a case where the angle of the right knee in a normal subject walking is calculated, and FIG. 9B calculates the angle of the right knee in a subject walking with a symptom of osteoarthritis of the knee. The case is illustrated.

  As shown in FIGS. 9A and 9B, the determination unit 142 calculates the angle of the right knee, assuming that the right knee is extended to 0 degrees. Specifically, the determination unit 142 calculates, as a right knee angle, an angle formed by a line segment connecting the joint 2n and the joint 2o with respect to a straight line passing through the joint 2m and the joint 2n. Here, if the subject is not knee osteoarthritis but a normal subject, the maximum angle of the right knee during walking is 60 degrees or more as shown in FIG. 9A. On the other hand, if the subject has symptoms of knee osteoarthritis, as shown in FIG. 9B, the maximum angle of the right knee during walking is less than 60 degrees. Specifically, in the case of a subject with grade “1” of osteoarthritis of the knee, the maximum angle of the right knee during walking is 50 degrees or more and less than 60 degrees. In the case of a subject with grade “2” of knee osteoarthritis, the maximum angle of the right knee during walking is 40 degrees or more and less than 50 degrees. Further, in the case of a subject of grade “3” of knee osteoarthritis, the maximum angle of the right knee during walking is 30 degrees or more and less than 40 degrees. In the case of a subject with grade “4” of knee osteoarthritis, the maximum angle of the right knee during walking is 25 degrees or more and less than 30 degrees. In the case of a subject with grade “5” of knee osteoarthritis, the maximum angle of the right knee during walking is 20 degrees or more and less than 25 degrees. Therefore, the determination unit 142 determines the grade of knee osteoarthritis of the subject according to which range the calculated angle of the right knee of the subject is included. For example, if the angle of the right knee of the subject is 45 degrees, the determination unit 142 determines that the subject is grade “2” of knee osteoarthritis.

  Note that the determination processing illustrated in FIGS. 9A and 9B is merely an example, and the present invention is not limited to this. For example, the determination unit 142 may determine using the angle of the left knee or may determine using the angle of the knees of both legs. Further, the determination unit 142 may calculate the angle of the knee with the knee extended as 180 degrees. Moreover, although the case where the disease information corresponding to the evaluation value of the disease closest to the evaluation value is determined has been described here, the present invention is not limited to this. For example, depending on the evaluation value, a value farthest from the evaluation value or an intermediate value may be used. Further, the calculated evaluation value may be determined by comparing or inferring with another index or the like. Although the case where the determination unit 142 uses motion information in walking training has been described here, the determination is not limited to this, and determination may be made using motion information when the subject is squatting. You may determine using the operation | movement information when raising / lowering.

  Moreover, the determination part 142 may determine using not only a knee angle but another evaluation value. For example, the determination unit 142 may determine the other evaluation value using a walking speed during walking. Specifically, the determination unit 142 calculates the maximum walking speed of each grade from the motion information of each grade of knee osteoarthritis. Then, the determination unit 142 calculates the maximum walking speed from the motion information of the subject, and determines the closest value grade as the disease information of the subject.

  Moreover, the determination part 142 may determine using the magnitude | size of the lateral blur of the knee at the time of a walk as another evaluation value. Specifically, when the subject walks from the back in the depth direction toward the front, the determination unit 142 determines the maximum in the x-axis direction of each grade from the motion information of each grade of knee osteoarthritis. The amount of change is calculated. Then, the determination unit 142 calculates the maximum change amount in the x-axis direction from the motion information of the subject, and determines the closest value grade as the disease information of the subject.

(Judgment process using trajectory)
Next, determination processing using a locus will be described. For example, the determination unit 142 compares the predetermined joint trajectory included in the motion information of the subject with the predetermined joint trajectory included in the disease motion information stored in the disease motion information storage unit 132, respectively. The disease information corresponding to the locus closest to the predetermined joint locus of the specimen is determined.

  10A and 10B are diagrams for explaining the determination process using the trajectory according to the first embodiment. 10A and 10B illustrate a case where the determination unit 142 determines the grade of knee osteoarthritis using the locus of the right knee joint (2n) during walking. 10A and 10B, the horizontal axis corresponds to the x-axis direction, and the vertical axis corresponds to the z-axis direction. That is, FIG. 10A and FIG. 10B illustrate knee trajectories on a horizontal plane. FIG. 10A illustrates the trajectory of the right knee during normal subject walking, and FIG. 10B illustrates the right knee trajectory during walking of a subject with osteoarthritic symptoms. 10A and 10B illustrate the case where the subject walks from the back in the depth direction toward the front.

  As shown in FIGS. 10A and 10B, the determination unit 142 plots the x coordinate and z coordinate of the right knee included in the disease motion information of each grade stored in the disease motion information storage unit 132 on a horizontal plane, The trajectory of the right knee of each grade on the horizontal plane is drawn. Here, if the subject is not a knee osteoarthritis but a normal subject, as shown in FIG. On the other hand, if the subject has symptoms of knee osteoarthritis, as shown in FIG. The determination unit 142 plots the x-coordinate and z-coordinate of the right knee included in the motion information of the subject acquired by the acquisition unit 141 on the horizontal plane, and draws the trajectory of the right knee of the subject on the horizontal plane. Then, the determination unit 142 compares the rendered right knee locus of the subject with the right knee locus of each grade by pattern matching. Then, the determination unit 142 determines the grade most similar to the trajectory of the subject's right knee as the grade of the subject's knee osteoarthritis.

  In addition, the determination process shown to FIG. 10A and 10B is an example to the last, and is not limited to this. For example, the determination unit 142 may determine using the locus of the left knee or may determine using the locus of the knees of both feet. Further, the determination unit 142 may determine using a foot locus. Further, the determination unit 142 does not necessarily depict the joint trajectory.

  Moreover, although the case where it determined using a locus | trajectory was illustrated here, it is not limited to this. For example, matching may be performed by comparing the positional relationship between two or more points in the trajectory. For example, the positional relationship is obtained by plotting coordinates at different timings for a predetermined joint on the xz plane. As an example, the determination unit 142 plots the knee coordinates of 0 seconds and the knee coordinates of 3 seconds on the xz plane after the subject starts walking training. Subsequently, the determination unit 142 plots the 0 second knee coordinates and the 3 second knee coordinates of the disease motion information on the xz plane. And the determination part 142 determines the disease information of a subject by comparing the difference of the x direction of these plotted points, and the difference of az direction.

  As described above, the determination unit 142 determines the disease information of the subject using either the determination process using the evaluation value or the determination process using the trajectory. Then, the determination unit 142 outputs the determination result to the output control unit 143.

  Note that the processing of the determination unit 142 is not limited to this. For example, the disease information of the subject may be determined using a determination process using the evaluation value and a determination process using the trajectory. Specifically, the determination unit 142 may perform a comprehensive evaluation of the determination result determined by the determination process using the evaluation value and the determination result determined by the process using the trajectory.

  For example, in addition to the above determination process, the determination unit 142 may further determine using a pain sign of the subject. Here, the pain sign is, for example, the distortion of facial expression of the subject or the word “pain”. In this case, the determination unit 142 stores in advance characteristics of facial expression distortion when the subject feels pain. Then, the determination unit 142 extracts facial distortion characteristics from the color image information included in the motion information of the subject, and counts the number of extractions. Further, the determination unit 142 extracts facial distortion characteristics from color image information included in the motion information of each grade, and counts the number of extractions (or extraction frequency). Then, the determination unit 142 determines the grade of the knee osteoarthritis of the subject by comparing the number of extractions of the pain sign of the subject and the number of extractions of the pain sign in each grade. If the grade determined using the evaluation value matches the grade determined using the pain sign, the determining unit 142 outputs the grade to the output control unit 143 as the grade of the subject. If they do not match, the determination unit 142 outputs the grade determined using the evaluation value to the output control unit 143 as the grade of the subject. Further, the determination unit 142 extracts the word “pain” from the speech recognition result included in the motion information of the subject, and determines the grade of the knee osteoarthritis of the subject using the number of extractions. good.

  The output control unit 143 outputs diagnosis support information based on the disease information of the subject. For example, the output control unit 143 refers to the diagnosis support information storage unit 133 and causes the output unit 110 to display diagnosis support information corresponding to the disease information of the subject determined by the determination unit 142. Specifically, the output control unit 143 causes the output unit 110 to display at least one of information indicating that there is a suspicion of disease, imaging related information, and interpretation points.

  For example, the output control unit 143 causes the output unit 110 to display information indicating that there is a suspicion of a disease corresponding to the disease information of the subject as diagnosis support information. Specifically, when the determination unit 142 determines that the subject has a grade “2” of osteoarthritis of the knee, the output control unit 143 has a suspicion of “grade“ 2 ”of the knee osteoarthritis. “There is a message” is displayed on the output unit 110. As a result, the output control unit 143 can support a doctor's diagnosis or prompt the subject to consult a medical institution.

  Further, for example, the output control unit 143 causes the output unit 110 to display imaging related information corresponding to the disease information of the subject as diagnosis support information. Specifically, the output control unit 143 refers to the diagnosis support information storage unit 133 when the determination unit 142 determines that the subject is grade “2” of knee osteoarthritis, and determines the knee joint The imaging related information corresponding to the grade “2” of the disease is acquired. Then, the output control unit 143 causes the output unit 110 to display, for example, a message “Please perform a load X-ray inspection (front image)” using the acquired imaging-related information. Thereby, the output control unit 143 can notify the doctor and the laboratory technician of a specific inspection method and imaging conditions.

  For example, the output control unit 143 causes the output unit 110 to display an interpretation point corresponding to the disease information of the subject as diagnosis support information. Specifically, the output control unit 143 refers to the diagnosis support information storage unit 133 when the determination unit 142 determines that the subject is grade “2” of knee osteoarthritis, and determines the knee joint The interpretation point corresponding to the grade “2” of the disease is acquired. Then, the output control unit 143 causes the output unit 110 to display, for example, a message “Please check whether the joint space is narrowed (less than 3 mm)” using the acquired interpretation points. Thereby, the output control part 143 can notify a specific interpretation point (interpretation method and know-how) to a doctor and an interpretation doctor.

  As described above, the output control unit 143 causes the output unit 110 to display at least one of information indicating that there is a suspicion of a disease, imaging-related information, and interpretation points.

  FIG. 11 is a flowchart illustrating a processing procedure performed by the medical information processing apparatus according to the first embodiment. As illustrated in FIG. 11, the acquisition unit 141 acquires motion information of the subject (Step S101). For example, the acquisition unit 141 stores the stored skeleton information and the stored information every time the motion information collection unit 10 and the medical information processing apparatus 100 are turned on and one frame of skeleton information is stored in the motion information storage unit 131. The color image information of the frame corresponding to the skeleton information obtained is acquired from the motion information storage unit 131, respectively.

  Subsequently, the determination unit 142 determines the disease information of the subject by comparing the motion information of the subject and the disease motion information (step S102). For example, the determination unit 142 compares the motion information of the subject acquired by the acquisition unit 141 with the disease motion information stored in the disease motion information storage unit 132, and the disease most similar to the acquired motion information. Specify operation information. Then, the determination unit 142 determines the disease information associated with the specified disease operation information as the disease information of the subject. Then, the determination unit 142 outputs the determination result to the output control unit 143.

  Then, the output control unit 143 outputs diagnosis support information based on the subject disease information (step S103). For example, the output control unit 143 refers to the diagnosis support information storage unit 133, and information indicating that there is a disease, imaging related information, and interpretation points according to the disease information of the subject determined by the determination unit 142 At least one of them is displayed on the output unit 110.

  As described above, the medical information processing apparatus 100 according to the first embodiment stores disease information and disease operation information in association with each other. Then, the medical information processing apparatus 100 acquires the motion information of the subject. Then, the medical information processing apparatus 100 determines the disease information of the subject by comparing the motion information of the subject and the disease motion information. Then, the medical information processing apparatus 100 outputs diagnosis support information based on the determined disease information of the subject. For this reason, the medical information processing apparatus 100 can support diagnosis. For example, the medical information processing apparatus 100 can support a doctor's diagnosis even when diagnosing a disease such as knee osteoarthritis that is difficult to diagnose early.

  For example, the medical information processing apparatus 100 prompts the execution of the examination by displaying information indicating that there is a suspicion of a disease for the subject. For this reason, the medical information processing apparatus 100 carries out the examination for diseases (such as osteoarthritis of the knee) that are rarely conducted unless significant symptoms occur, even if no significant symptoms occur. Because it prompts, early diagnosis of the disease becomes possible.

  In addition, for example, the medical information processing apparatus 100 displays imaging-related information when urging to perform an examination. Therefore, the medical information processing apparatus 100 can notify a doctor and a laboratory technician of a specific inspection method and imaging conditions.

  In addition, for example, the medical information processing apparatus 100 displays an interpretation point when prompting to perform an examination. Therefore, the medical information processing apparatus 100 can notify a doctor and an interpreting doctor of specific interpretation points (interpretation method and know-how).

(Modification 1 of the first embodiment)
In the above embodiment, the case where the medical information processing apparatus 100 stores the motion information in the rehabilitation of a representative subject as the disease motion information has been described. Operation information in rehabilitation of the subject may be stored.

  In the disease motion information storage unit 132 according to the first modification of the first embodiment, the storage unit associates a plurality of the disease motion information of the subject and the attribute information of the subject for each disease information. Add and remember.

  FIG. 12 is a diagram illustrating an example of information stored in the disease motion information storage unit 132 according to the first modification of the first embodiment. As shown in FIG. 12, the disease motion information storage unit 132 stores information in which disease information, attribute information, and disease motion information are associated with each other. Here, “attribute information” indicates information representing the attributes of the subject, for example, information including the age and sex of the subject.

  As illustrated in FIG. 12, the disease motion information storage unit 132 includes a disease name “osteoarthritis of knee”, grade “0”, age “52”, gender “male”, motion information “color image information”. , Distance image information, voice recognition result, skeleton information, etc. That is, the disease motion information storage unit 132 stores motion information in the rehabilitation (gait training) of a 52-year-old man diagnosed with grade “0” of knee osteoarthritis. The disease motion information storage unit 132 includes a disease name “osteoarthritis of knee”, a grade “1”, an age “60”, a sex “female”, and motion information “color image information, distance image information, “Speech recognition result, skeleton information” and the like are stored. That is, the disease motion information storage unit 132 stores motion information in rehabilitation (walking training) of a 60-year-old woman diagnosed with a grade “1” of knee osteoarthritis. Similarly, the disease motion information storage unit 132 stores information in which the disease name, grade, age, sex, and motion information are associated with each other.

  The determination unit 142 according to the first modification of the first embodiment acquires the attribute information of the subject and compares the disease motion information corresponding to the acquired attribute information with the motion information of the subject, thereby obtaining the subject information. Determine the disease information of the specimen.

  For example, the determination unit 142 receives input of information including the attribute information of the subject, and acquires the attribute information of the subject from the received information. Specifically, when the health check is performed, the determination unit 142 receives input of information including the age “60” of the subject and the sex “female” from the operator, and from the received information, the age “ 60 ”and gender“ female ”. Note that the method by which the determination unit 142 acquires the attribute information of the subject is not limited to the above method, and may be acquired from, for example, an electronic medical record system.

  Subsequently, the determination unit 142 extracts disease motion information corresponding to the acquired attribute information from the disease motion information storage unit 132. Specifically, when the age of the acquired subject is “60” and the gender is “female”, the determination unit 142 extracts the disease motion information that matches the attribute information from the disease motion information storage unit 132. Then, the determination unit 142 determines the disease information of the subject by comparing the motion information of the subject acquired by the acquisition unit 141 with the extracted disease motion information. Note that the determination process by the determination unit 142 is the same as the above-described determination process, and thus description thereof is omitted.

  The determination process by the determination unit 142 is merely an example, and the present invention is not limited to this. For example, the determination unit 142 may extract the disease motion information corresponding to the closest attribute information if there is no attribute information that matches the acquired attribute information of the subject in the disease motion information storage unit 132. The determination unit 142 may extract a plurality of disease motion information from the disease motion information storage unit 132. In this case, for example, the determination unit 142 calculates an average value of each evaluation value calculated from the extracted plurality of disease motion information, and compares this with an evaluation value calculated from the motion information of the subject. The disease information of the subject is determined.

  As described above, the medical information processing apparatus 100 stores the disease operation information of each subject together with the attribute information of a plurality of subjects, and appropriately extracts the disease operation information corresponding to the attribute information of the subject. The disease information of the subject is determined. For this reason, the medical information processing apparatus 100 can determine the disease information of the subject more accurately.

(Modification 2 of the first embodiment)
In the above-described embodiment, the case where the medical information processing apparatus 100 stores the motion information in the rehabilitation of the subject as the disease motion information is described. However, the present invention is not limited thereto, and only the evaluation value is used. The grade may be determined.

  The disease motion information storage unit 132 according to the second modification of the first embodiment stores, for example, information in which disease information is associated with an evaluation value calculated from the disease motion information. Specifically, the disease motion information storage unit 132 stores a disease name “degenerative knee joint”, a grade “0”, and an evaluation value “60 degrees or more”. That is, the disease motion information storage unit 132 stores that the maximum angle during walking of the knee of a representative subject diagnosed with grade “0” of knee osteoarthritis is 60 degrees or more. The disease motion information storage unit 132 stores a disease name “degenerative knee joint disease”, a grade “1”, and an evaluation value “50 degrees or more and less than 60 degrees”. That is, the disease motion information storage unit 132 stores that the maximum angle during walking of the knee of a representative subject diagnosed with grade “0” of osteoarthritis of the knee is 50 degrees or more and less than 60 degrees. . Similarly, the disease motion information storage unit 132 stores information in which disease names, grades, and evaluation values are associated with each other.

  The determination unit 142 according to the second modification of the first embodiment calculates an evaluation value from the motion information of the subject acquired by the acquisition unit 141. Then, the determination unit 142 compares the calculated evaluation value of the subject with the evaluation value stored in the disease motion information storage unit 132, and the disease corresponding to the evaluation value of the disease closest to the evaluation value of the subject. Determine information. For this reason, the medical information processing apparatus 100 can support diagnosis with a smaller processing load.

(Second Embodiment)
In the above embodiment, the case where the medical information processing apparatus 100 displays a message as diagnosis support information has been described, but the present invention is not limited to this. For example, the medical information processing apparatus 100 may output diagnosis support information to an external device connected via a network, and support the diagnosis on the external device side. Therefore, in the second embodiment, a case will be described in which the medical information processing apparatus 100 outputs diagnosis support information to an external apparatus connected via a network and supports diagnosis on the external apparatus side.

  FIG. 13 is a block diagram illustrating a configuration example of the medical information processing system 1 according to the second embodiment. The medical information processing system 1 according to the second embodiment includes an operation information collection unit 10, a medical information processing device 100, a RIS (Radiology Information System) 20, a medical image storage device 30, and a medical image diagnostic device 40. And a workstation 50. Each apparatus is in a state where it can communicate with each other directly or indirectly by, for example, a hospital LAN (Local Area Network) 2 installed in the hospital. For example, when a PACS (Picture Archiving and Communication System) is introduced in the medical information system 1, each device transmits and receives medical image data and the like according to the DICOM (Digital Imaging and Communications in Medicine) standard.

  The motion information collection unit 10 and the medical information processing apparatus 100 have the same functions as the motion information collection unit 10 and the medical information processing apparatus 100 described in the first embodiment. In the second embodiment, the motion information collection unit 10 and the medical information processing apparatus 100 are used, for example, in a rehabilitation department that performs rehabilitation of a subject.

  For example, the movement information collection unit 10 collects movement information on rehabilitation performed by the subject in the rehabilitation department, and transmits the collected movement information to the medical information processing apparatus 100. The medical information processing apparatus 100 receives the motion information of the subject collected by the motion information collection unit 10, and uses the received motion information of the subject to acquire the acquisition unit 141 and the determination described in the first embodiment. Each process of the unit 142 is executed. Then, based on the determination result determined by the determination unit 142, the medical information processing apparatus 100 generates diagnosis support information for supporting diagnosis by the external device, and transmits the generated diagnosis support information to the external device. The processing of the medical information processing apparatus 100 will be described later.

  The RIS 20 is a device that manages examination reservations. For example, the RIS 20 receives an input of an examination reservation order for reserving an examination of a subject by a doctor or an office worker working in a hospital, and transmits the accepted examination reservation order to each device of the medical information system 1.

  The medical image storage device 30 is a device that manages medical image data. For example, the medical image storage device 30 includes a database that stores medical image data, stores medical image data and examination results generated by the medical image diagnostic device 40 described later in the database, and stores them. The medical image storage device 30 may be integrated with a workstation 50 described later by using a workstation capable of storing a large-capacity image.

  The medical image diagnostic apparatus 40 includes an X-ray diagnostic apparatus, an X-ray CT apparatus, an MRI apparatus, an ultrasonic diagnostic apparatus, a SPECT (Single Photon Emission Computed Tomography) apparatus, a PET (Positron Emission computed Tomography) apparatus, a SPECT apparatus, and an X-ray CT. The apparatus is a SPECT-CT apparatus in which the apparatus is integrated, a PET-CT apparatus in which the PET apparatus and the X-ray CT apparatus are integrated, a specimen inspection apparatus, or the like. For example, the medical image diagnostic apparatus 40 images a subject in response to an operation from a laboratory technician who images the subject, and generates medical image data and a test result.

  The workstation 50 is an image processing apparatus that performs image processing on medical image data. For example, the workstation 50 acquires medical image data and test results from the medical image storage device 30, and displays the acquired medical image data and test results on a monitor.

  With such a configuration, the medical information processing apparatus 100 according to the second embodiment supports diagnosis by an external apparatus such as the medical image diagnostic apparatus 40 and the workstation 50 by the processing described below.

(Automatic setting of imaging method and imaging conditions in the medical image diagnostic apparatus 40)
An imaging method and automatic setting of imaging conditions in the medical image diagnostic apparatus 40 will be described. In the medical information processing apparatus 100 according to the second embodiment, the output control unit 143 sets an imaging method and imaging conditions for imaging for image diagnosis according to disease information based on the disease information of the subject. Is output to the medical image diagnostic apparatus 40 that performs image diagnosis. This setting information is an example of diagnosis support information.

  For example, the output control unit 143 according to the second embodiment refers to the diagnosis support information storage unit 133 when the determination unit 142 determines that the subject is grade “2” of knee osteoarthritis, Imaging related information corresponding to grade “2” of knee osteoarthritis is acquired. And the output control part 143 produces | generates the setting information which sets an imaging | photography method and imaging conditions to the medical image diagnostic apparatus 40 using the acquired imaging | photography related information. This setting information includes, for example, the disease information of the subject, information specifying the type of medical image diagnostic apparatus 40 (for example, X-ray diagnostic apparatus), and imaging conditions (for example, load X-ray examination (front image)). including. Then, the output control unit 143 adds the examination information to the generated setting information and outputs it to the medical image diagnostic apparatus 40 (X-ray diagnostic apparatus) specified in the setting information. This examination information includes information such as the name of the subject, the patient ID for identifying the subject (patient), and the examination ID for identifying the examination. The examination information may be manually input by an operator of the medical information processing apparatus 100 or may be acquired from an electronic medical record system or the like.

  Then, the medical image diagnostic apparatus 40 acquires setting information output from the output control unit 143, and sets an imaging method and imaging conditions for image diagnosis based on the acquired setting information. For example, when the medical image diagnostic apparatus 40 acquires the setting information from the output control unit 143, the medical image diagnostic apparatus 40 sets the acquired content as the imaging condition for the inspection using the inspection information attached to the setting information.

  As described above, the medical information processing apparatus 100 according to the second embodiment supports diagnosis by outputting setting information for setting an imaging method and imaging conditions to the medical image diagnostic apparatus 40 as diagnosis support information. can do.

  For example, the setting information may be output to the medical image diagnostic apparatus 40 after passing through the RIS 20. For example, the output control unit 143 of the medical information processing apparatus 100 outputs setting information to the RIS 20 as diagnosis support information. When the RIS 20 receives the setting information output from the output control unit 143, the RIS 20 displays the setting information on the display unit. In the RIS 20, an operator (doctor or office worker) of the RIS 20 makes a reservation for the medical image diagnostic apparatus 40 that performs image diagnosis while browsing the displayed information. For example, the operator selects the medical image diagnostic apparatus 40 that performs the image diagnosis, and performs an operation in the RIS 20 to specify the selected medical image diagnostic apparatus 40 and the interpretation doctor. The RIS 20 attaches information indicating the medical image diagnostic apparatus 40 designated by the operator and information indicating the interpretation doctor to the setting information, and the medical image diagnostic apparatus that performs image diagnosis on the setting information attached with various information Output to 40. Upon receiving the setting information output from the RIS 20, the medical image diagnostic apparatus 40 sets an imaging method and imaging conditions for image diagnosis based on the received setting information.

  In addition, the process in which the medical information processing apparatus 100 supports the diagnosis with the medical image diagnostic apparatus 40 is not limited to the above-described process. For example, the imaging corresponding to the disease information of the subject using the imaging related information is used. The related information may be displayed on the medical image diagnostic apparatus 40. Specifically, when the determination unit 142 determines that the subject is grade “2” of knee osteoarthritis, the medical information processing apparatus 100 performs “load X-ray examination (front image)”. Is generated on the display unit of the medical image diagnostic apparatus 40. Then, the medical information processing apparatus 100 outputs the generated diagnosis support information to the medical image diagnostic apparatus 40, and displays a message “Please perform a load X-ray examination (front image)” on the display unit of the medical image diagnostic apparatus 40. To display. Thereby, the output control unit 143 can notify the doctor and the laboratory technician of a specific inspection method and imaging conditions.

(Display of interpretation points on workstation 50)
The display of interpretation points on the workstation 50 will be described. In the medical information processing apparatus 100 according to the second embodiment, the output control unit 143 is a display for causing the workstation 50 to display a point when an imaged image related to disease information is interpreted based on the disease information of the subject. Information is output to the workstation 50. This display information is an example of diagnosis support information.

  For example, the output control unit 143 according to the second embodiment refers to the diagnosis support information storage unit 133 when the determination unit 142 determines that the subject is grade “2” of knee osteoarthritis, The interpretation point “is the joint space narrow (less than 3 mm) seen” corresponding to grade “2” of knee osteoarthritis is acquired. Then, the output control unit 143 generates display information for causing the workstation 50 to display the acquired interpretation points. Then, the output control unit 143 outputs the generated display information to the medical image diagnostic apparatus 40 (X-ray diagnostic apparatus). The medical image diagnostic apparatus 40 that is the output destination of the display information may be designated by the operator of the medical information processing apparatus 100, or is automatically selected based on imaging related information corresponding to the disease information of the subject. May be.

  Then, the medical image diagnostic apparatus 40 receives display information output from the output control unit 143. Then, when the medical image diagnostic apparatus 40 captures an image for image diagnosis, it adds the received display information as supplementary information of the captured image. Then, the medical image diagnostic apparatus 40 outputs the captured image with the display information attached to the medical image storage apparatus 30 and stores it.

  The workstation 50 acquires the captured image of the subject and the display information attached to the captured image from the medical image storage device 30, and displays the acquired captured image and display information on the display unit.

  For example, the workstation 50 acquires a captured image captured by the medical image diagnostic apparatus 40 from the medical image storage apparatus 30 based on an instruction from an operator (for example, a doctor or an interpretation doctor) of the workstation 50. Then, the workstation 50 acquires display information attached to the captured image from the captured image. Then, the workstation 50 displays the interpretation points included in the display information together with the captured image on the display unit. For example, the workstation 50 displays, on the monitor of the workstation 50, a message “Please check if the joint space is narrowed (less than 3 mm)” along with the photographed image of the load X-ray inspection (front image). Let As described above, the medical information processing apparatus 100 can support diagnosis even in a terminal on which a captured image is displayed.

(Cooperation with analysis program on workstation 50 (CAD collaboration))
The cooperation with the analysis program in the workstation 50 will be described. In this case, the workstation 50 functions as a computer-aided diagnosis device (Computer-Aided Diagnosis: CAD device). For example, the workstation 50 includes a CAD program such as a program for executing a predetermined process. Then, the workstation 50 performs predetermined processing on the captured image (medical image) obtained by capturing the subject according to the diagnosis information of the subject determined by the medical information processing apparatus 100.

  For example, the workstation 50 according to the second embodiment stores, as diagnosis support information, information indicating that a predetermined process is executed in a CAD program for each disease information. As an example, the workstation 50 stores information indicating that a measurement process for measuring the narrowing of the joint space is performed on a photographed image of a subject of grade “2” of knee osteoarthritis. As another example, information indicating that the workstation 50 performs a measurement process for measuring wear or loss of a load surface on a photographed image of a subject with grade “4” of knee osteoarthritis. Remember.

  In the medical information processing apparatus 100 according to the second embodiment, the output control unit 143 transmits the determination result determined by the determination unit 142 to the medical image storage device 30 that captures a medical image of the subject. For example, when the determination unit 142 determines that the subject has a knee osteoarthritis grade “2”, the output control unit 143 receives information indicating that the knee osteoarthritis has a grade “2”. The image is transmitted to the medical image diagnostic apparatus 40 in association with the sample identification information.

  The medical image diagnostic apparatus 40 receives the disease information of the subject from the medical information processing apparatus 100. Then, the medical information processing apparatus 100 captures the subject and generates a captured image of the subject. Then, the medical image diagnostic apparatus 40 appends the received disease information as supplementary information of the generated captured image, and outputs and stores the medical information in the medical image storage apparatus 30.

  The workstation 50 acquires a captured image of the subject from the medical image storage device 30, and executes predetermined processing on the captured image based on the symptom information of the subject attached to the acquired captured image.

  For example, the workstation 50 acquires the captured image of the subject from the medical image storage device 30 when displaying the captured image of the subject. Then, the workstation 50 acquires information indicating that the subject is grade “2” of knee osteoarthritis from the incidental information of the acquired captured image. Then, the workstation 50 reads information indicating that a predetermined process is to be executed on the CAD program from the above information stored in the own device. For example, the workstation 50 uses information indicating that the subject is grade “2” of knee osteoarthritis, so that the captured image of the subject of grade “2” of knee osteoarthritis is obtained. Information indicating that the measurement process for measuring the narrowing of the joint space is performed is read. Then, the workstation 50 performs a measurement process for measuring the narrowing of the joint space with respect to the captured image of the subject. The workstation 50 displays the measurement result on the monitor together with the captured image. As described above, the medical information processing apparatus 100 can support diagnosis even in a terminal on which a captured image is displayed. The medical image diagnostic apparatus 40 may output the captured image workstation 50 with the disease information.

  In the above example, the case where the measurement process is executed has been described, but the embodiment is not limited thereto. For example, the workstation 50 may be set to initially display (jump) a captured image corresponding to disease information among the plurality of captured images when a plurality of captured images exist for the subject. In this case, the workstation 50 stores a diagnosis support information storage unit 133. The workstation 50 acquires imaging related information corresponding to the disease information of the subject from the diagnosis support information storage unit 133. Then, the workstation 50 is set so as to initially display a captured image corresponding to the imaging related information among the plurality of captured images of the subject. For example, the workstation 50 is set to initially display the front image of the load X-ray examination by using information that the subject is grade “2” of knee osteoarthritis. Thereby, when the workstation 50 receives an instruction to display the captured image of the subject, the workstation 50 can initially display the captured image corresponding to the disease information.

  Further, for example, the workstation 50 may detect and display an image area to be interpreted from a captured image of the subject. In this case, the workstation 50 stores, as diagnosis support information, feature information of the image area to be interpreted for each disease information. When the workstation 50 acquires the disease information of the subject, the workstation 50 reads the feature information corresponding to the disease information. Then, the workstation 50 detects an image area corresponding to the feature information from the captured image of the subject. Then, the workstation 50 displays the identified image area with attention. For example, the workstation 50 detects an image region including a joint space by using information indicating that the subject is grade “2” of knee osteoarthritis, and enlarges and displays the image region. It can be displayed with a red border.

  Further, for example, the workstation 50 may display a typical image of a disease in parallel with a captured image as an image useful for interpretation. In this case, the workstation 50 stores a typical image of a disease for each disease information as diagnosis support information. For example, the workstation 50 stores an image in which the joint space is reduced by 3 mm as a typical image of grade “2” of knee osteoarthritis. Then, when the workstation 50 acquires the disease information of the subject, the workstation 50 reads a typical image of the disease corresponding to the disease information. The workstation 50 displays a typical image of this disease in parallel with the captured image of the subject. For example, the workstation 50 can display, in parallel with the captured image, an image in which the joint space is narrowed by 3 mm by using information that the subject is grade “2” of knee osteoarthritis.

  In the second embodiment, the case where the imaging method and the imaging conditions are automatically set in the medical image diagnostic apparatus 40 and the case where the interpretation point is displayed on the workstation 50 have been described. However, the present invention is not limited to this. is not. For example, you may implement combining these. Specifically, the medical information processing apparatus 100 outputs the setting information and the display information to the medical image diagnostic apparatus 40. The medical image diagnostic apparatus 40 automatically sets the imaging method and imaging conditions, adds display information to the captured image, and stores it in the medical image storage apparatus 30. The workstation 50 displays the captured image together with the display information.

(Other embodiments)
Although the first and second embodiments have been described so far, the present invention may be implemented in various different forms other than the above-described embodiments.

(Use of motion information already taken)
For example, in the first embodiment described above, the case where the medical information processing apparatus 100 processes the motion information of the subject collected by the motion information collection unit 10 in real time and outputs diagnosis support information has been described. The embodiment is not limited to this. For example, the medical information processing apparatus 100 may acquire motion information of a subject that has been imaged from an external device such as a database, and output diagnosis support information using the motion information.

(Use of disease motion information)
Further, for example, the medical information processing apparatus 100 may output information based on the disease motion information corresponding to the disease information by using information stored in the disease motion information storage unit 132.

  As an example, the medical image diagnostic apparatus 40 that is an X-ray diagnostic apparatus measures joint space (for example, 2 mm) using an existing analysis program when a load X-ray examination is performed. Then, the medical image diagnostic apparatus 40 outputs the measurement result to the medical information processing apparatus 100.

  When the determination unit 142 of the medical information processing apparatus 100 receives the measurement result output from the medical image diagnostic apparatus 40, the determination unit 142 refers to the diagnosis support information storage unit 133 and determines the disease information of the subject. For example, if the joint space is 2 mm, the determination unit 142 corresponds to the interpretation point “whether the joint space is narrowed (less than 3 mm)” in the diagnosis support information storage unit 133. It is determined that the disease is grade “2”. Then, the determination unit 142 outputs the determination result to the output control unit 143.

  The output control unit 143 of the medical information processing apparatus 100 generates diagnosis support information based on the disease operation information corresponding to the disease information. For example, the output control unit 143 calculates a walking speed (for example, 2 km / h) from the disease motion information (for example, motion information of walking training) of grade “2” of knee osteoarthritis. The output control unit 143 causes the output unit 110 to display the calculated walking speed as diagnosis support information. As described above, the medical information processing apparatus 100 outputs the walking speed when the subject (patient) of the knee osteoarthritis grade “2” performs rehabilitation, for example, assistance for assisting the subject. The person can be notified of a guideline of the walking speed according to the subject.

  Note that information output as diagnosis support information is not limited to walking speed. For example, the medical information processing apparatus 100 may output a moving image showing the state of rehabilitation as diagnosis support information based on a color image included in the disease motion information. According to this, for example, a physical therapist or a caregiver confirms a rehabilitation video, for example, details of whether walking training is performed on a flat floor or on stairs, etc. You can check the information.

(Application to service providing equipment)
FIG. 14 is a diagram for explaining an example when applied to a service providing apparatus. As illustrated in FIG. 14, the service providing apparatus 200 is disposed in the service center and connected to, for example, a medical institution, a terminal apparatus 300 disposed at home, or a workplace via the network 5. The operation information collection unit 10 is connected to each of the terminal devices 300 disposed in the medical institution, home, and workplace. Each terminal device 300 includes a client function that uses a service provided by the service providing device 200. The network 5 may employ any type of communication network such as the Internet or WAN (Wide Area Network) regardless of whether it is wired or wireless.

  For example, the service providing apparatus 200 has a function similar to that of the medical information processing apparatus 100 described with reference to FIG. 5 and provides the terminal apparatus 300 as a service using the function. That is, the service providing apparatus 200 includes functional units similar to the acquisition unit 141, the determination unit 142, and the output control unit 143, respectively. A functional unit similar to the acquisition unit 141 acquires motion information representing the motion of the subject. A functional unit similar to the determination unit 142 determines the disease information of the subject by comparing the acquired motion information with the disease motion information. A function unit similar to the output control unit 143 outputs diagnosis support information that supports diagnosis of a disease based on the disease information of the subject. The service providing apparatus 200 stores the same information as the information stored in the disease motion information storage unit 132.

  For example, the service providing apparatus 200 receives an upload of a frame group of operation information to be processed from the terminal apparatus 300. Then, the service providing apparatus 200 performs the above processing to generate diagnosis support information. Then, the service providing apparatus 200 causes the terminal apparatus 300 to download diagnosis support information.

  Further, the configuration of the medical information processing apparatus 100 in the above-described embodiment is merely an example, and the integration and separation of each unit can be performed as appropriate. For example, the determination unit 142 and the output control unit 143 can be integrated.

  Further, the functions of the acquisition unit 141, the determination unit 142, and the output control unit 143 described in the above-described embodiments can be realized by software. For example, the functions of the acquisition unit 141, the determination unit 142, and the output control unit 143 are medical information processing that defines the processing procedure described as being performed by the acquisition unit 141, the determination unit 142, and the output control unit 143 in the above embodiment. This is realized by causing the computer to execute the program. The medical information processing program is stored in, for example, a hard disk or a semiconductor memory device, and is read and executed by a processor such as a CPU or MPU. The medical information processing program is recorded on a computer-readable recording medium such as a CD-ROM (Compact Disc-Read Only Memory), an MO (Magnetic Optical disk), a DVD (Digital Versatile Disc), and distributed. obtain.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

  According to at least one embodiment described above, the medical information processing apparatus, system, and program of this embodiment can support diagnosis.

DESCRIPTION OF SYMBOLS 100 Medical information processing apparatus 141 Acquisition part 142 Determination part 143 Output control part

Claims (13)

A storage unit that stores symptom information that represents symptoms , degree information that represents the degree of the symptoms , and symptom movement information that represents the behavior of the subject in accordance with the degree of the symptoms;
An acquisition unit for acquiring motion information representing the motion of the subject;
A determination unit that determines the symptom information and the degree information of the subject by comparing the movement information and the symptom movement information;
Based on the symptoms information and extent information of the subject determined by the determination unit, and an output control unit for outputting diagnostic support information related to diagnostic imaging for performing definitive diagnosis of the determined condition information and degree information A medical information processing apparatus characterized by that. The determination unit calculates an evaluation value of the subject from the motion information, calculates an evaluation value of a symptom from the symptom motion information, and calculates the symptom information and degree information based on the evaluation value of the subject. The medical information processing apparatus according to claim 1, wherein the determination is performed. The determination unit compares the position information at a predetermined timing with respect to the predetermined joint included in the motion information and the position information of the predetermined joint included in the symptom movement information stored in the storage unit. The medical information processing apparatus according to claim 1, wherein symptom information and degree information corresponding to position information closest to the position information of a predetermined joint of a specimen are determined. The storage unit stores, for each symptom information, a plurality of the symptom operation information of the subject and the attribute information of the subject in association with each other,
The determination unit determines the symptom information and degree information of the subject by acquiring the attribute information of the subject and comparing the symptom motion information corresponding to the attribute information with the motion information of the subject. The medical information processing apparatus according to any one of claims 1 to 3, wherein:   The output control unit causes the display unit to display information indicating that there is a suspicion of a symptom corresponding to the symptom information of the subject as the diagnosis support information. The medical information processing apparatus according to one.   The output control unit causes the display unit to display information representing at least one of an imaging method and an imaging condition according to the symptom information of the subject as the diagnosis support information. The medical information processing apparatus according to any one of the above.   The output control unit causes the display unit to display a point at the time of interpreting a captured image related to the symptom information of the subject as the diagnosis support information. The medical information processing apparatus described.   The output control unit generates diagnosis support information based on symptom operation information corresponding to the symptom information, and outputs the generated diagnosis support information. The medical information processing apparatus described. In a medical information processing system including a medical information processing apparatus and a medical image diagnostic apparatus,
The medical information processing apparatus includes:
A storage unit that stores symptom information that represents symptoms , degree information that represents the degree of the symptoms , and symptom movement information that represents the behavior of the subject in accordance with the degree of the symptoms;
An acquisition unit for acquiring motion information representing the motion of the subject;
A determination unit that determines the symptom information and the degree information of the subject by comparing the movement information and the symptom movement information;
Based on the symptom information and degree information of the subject determined by the determination unit, at least one of an imaging method and imaging conditions related to image diagnosis for performing a definitive diagnosis of the determined symptom information and degree information is set An output control unit for outputting setting information for performing the setting to the medical image diagnostic apparatus,
The medical image diagnostic apparatus comprises:
The medical information processing system, wherein the setting information output from the output control unit is acquired, and at least one of the imaging method and the imaging condition is set based on the acquired setting information. In a medical information processing system including a first medical information processing device and a second medical information processing device,
The first medical information processing apparatus includes:
A storage unit that stores symptom information that represents symptoms , degree information that represents the degree of the symptoms , and symptom movement information that represents the behavior of the subject in accordance with the degree of the symptoms;
An acquisition unit for acquiring motion information representing the motion of the subject;
A determination unit that determines the symptom information and the degree information of the subject by comparing the movement information and the symptom movement information;
Based on the symptom information and degree information of the subject determined by the determination unit, the points at the time of interpreting a captured image relating to image diagnosis for performing a definitive diagnosis of the determined symptom information and degree information are the first An output control unit that outputs display information for display on the medical information processing apparatus 2 to the medical image diagnostic apparatus,
The second medical information processing apparatus includes:
A medical information processing system that acquires a captured image captured by the medical image diagnostic apparatus and the display information attached to the captured image, and displays the acquired captured image and display information. In a medical information processing system including a first medical information processing device, a medical image diagnostic device, and a second medical information processing device,
The first medical information processing apparatus includes:
A storage unit that stores symptom information that represents symptoms , degree information that represents the degree of the symptoms , and symptom movement information that represents the behavior of the subject in accordance with the degree of the symptoms;
An acquisition unit for acquiring motion information representing the motion of the subject;
A determination unit that determines the symptom information and the degree information of the subject by comparing the movement information and the symptom movement information;
An output control unit that transmits the symptom information and degree information of the subject determined by the determination unit to a medical image diagnostic apparatus that generates a medical image of the subject;
The medical image diagnostic apparatus comprises:
Generating a medical image of the subject, and attaching the symptom information and degree information of the subject received from the first medical information processing apparatus to the generated medical image;
The second medical information processing apparatus includes:
Measurement for acquiring a medical image of the subject and measuring a part related to the operation of the subject on the medical image based on the symptom information and degree information of the subject attached to the acquired medical image medical information processing system and executes the processing. An acquisition unit for acquiring motion information representing the motion of the subject;
A determination unit that determines a symptom degree based on an evaluation value calculated from the motion information;
A medical information processing apparatus, comprising: an output control unit that outputs diagnosis support information related to image diagnosis for performing a definitive diagnosis of the determined symptom level according to the symptom level. An acquisition procedure for acquiring motion information representing the motion of the subject;
The symptom operation information stored in a storage unit that stores symptom information indicating symptoms , degree information indicating the degree of symptoms , and symptom operation information indicating the action of the subject according to the degree of symptoms And a determination procedure for determining the symptom information and degree information of the subject by comparing the operation information acquired by the acquisition procedure,
Wherein based on the subject condition information and extent information determined by the determining step, and an output control procedure for outputting diagnostic support information related to diagnostic imaging for performing a definitive diagnosis of the determined said condition information and extent information A medical information processing program that is executed by a computer.

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