WO2023013906A1 - Pressure calculation method and apparatus for ventriculoperitoneal shunting - Google Patents
Pressure calculation method and apparatus for ventriculoperitoneal shunting Download PDFInfo
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Definitions
- the present invention relates to a method and apparatus for calculating pressure for ventricle-abdominal shunting, and more particularly, to a ventricle-abdominal shunting method capable of automatically calculating and providing pressure for ventricle-abdominal shunting based on a plurality of medical images. It relates to a pressure calculation method and device for
- the amount of cerebrospinal fluid (cerebrospinal fluid) surrounding the brain and spinal cord or in the ventricle is about 140 ml, about 25 ml in the ventricle, and the normal cerebrospinal fluid pressure is less than 150 mmH 2 O, and 400 to 500 ml per day at 0.35 ml per minute is produced.
- Absorption of cerebrospinal fluid is achieved by a pressure difference of about 7mmH 2 O between the subarachnoid pressure and the dural sinus through the arachnoid villi of the dural sinus (dural venous sinus).
- Hydrocephalus is caused by causes such as excessive production of cerebrospinal fluid, obstruction of cerebrospinal fluid, and impaired venous drainage.
- ventriculoperitoneal shunting is an operation performed to treat various causes of hydrocephalus. It is a surgery to insert a catheter of a shunt system into the abdominal cavity using a separate incision in the abdomen by perforating the skull, puncturing the ventricles, and tunneling the subcutaneous tissue therefrom. At this time, in order to control the amount of cerebrospinal fluid drained from the ventricle to the abdominal cavity, a pressure control valve device or a flow control valve device is provided between the ventricle and the abdominal cavity to control the amount of discharged fluid (discharge amount) of the cerebrospinal fluid.
- the present invention has been proposed to solve the above problems, and based on an artificial intelligence model, a data set composed of input values corresponding to a plurality of medical images is learned, and the input medical image is obtained using the learning data. It is to provide a pressure calculation method and device for ventricular-peritoneal shunting that can automatically calculate and provide an input value suitable for
- the present invention automatically calculates a highly reliable input value using normal operation data of other patients without the need for the user to visually check the patient's medical image and determine the input value depending on experience, thereby erroneous pressure values. It is to provide a pressure calculation method and apparatus for ventricle-peritoneal shunting that can minimize the occurrence of symptoms or complications that may occur as a result of applying the.
- a pressure calculation method for ventricle-peritoneal shunting for solving the above problems includes collecting a plurality of medical image data; generating a dataset by checking pressure values labeled on each of the plurality of collected medical image data; Performing learning on an artificial intelligence model using the generated dataset; and calculating a pressure value corresponding to the input medical image data using the learned artificial intelligence model when the medical image data is input, wherein the pressure value is transmitted from a first catheter inserted into the ventricle to the abdominal cavity. Determining the amount of cerebrospinal fluid drained through the inserted second catheter may be applied to a pressure control valve provided between the first catheter and the second catheter.
- a pressure calculating device for ventricular-peritoneal shunting includes a communication module for transmitting and receiving at least one piece of information or data; A learning module for learning an artificial intelligence model using a dataset; a storage module for storing various types of information or data and storing at least one process required to calculate a pressure value corresponding to the patient's medical image data; and a control module that performs an operation for calculating a pressure value corresponding to the medical image data of the patient based on the at least one process, wherein the control module collects a plurality of medical image data, A data set is created by checking the pressure value labeled on each of the plurality of collected medical image data, and learning is performed on the artificial intelligence model using the generated data set.
- a pressure value corresponding to the input medical image data is calculated using the artificial intelligence model, and the pressure value determines the amount of cerebrospinal fluid drained from the first catheter inserted into the ventricle to the second catheter inserted into the abdominal cavity. As such, it can be applied to a pressure control valve provided between the first catheter and the second catheter.
- a dataset composed of input values corresponding to each of a plurality of medical images is learned based on an artificial intelligence model, and input values suitable for the patient's medical images are automatically calculated using the learning data.
- the user does not have to visually check the patient's medical image and determine the input value depending on experience, but automatically calculates a highly reliable input value using normal operation data of other patients, Minimize the occurrence of symptoms or complications that may occur by applying the pressure value.
- FIG. 1 is a diagram for a brief description of ventricle-peritoneal shunting.
- FIG. 2 is a view for explaining an x-ray image as medical image data of a patient who has undergone ventricular-abdominal cavity shunting and its structure.
- FIG. 3 is a block diagram showing the configuration of a pressure calculation system for ventricular-peritoneal shunting according to an embodiment of the present invention.
- FIG. 4 is a diagram showing an example of a pressure gauge indicator for checking a pressure value in a pressure calculating device according to an embodiment of the present invention.
- FIG. 5 is a flow chart showing a pressure calculation method for ventricle-peritoneal shunting according to an embodiment of the present invention.
- FIG. 6 is a diagram illustrating an example in which notification information transmitted from a pressure calculating device according to an embodiment of the present invention is displayed through a display unit of a manager terminal.
- spatially relative terms “below”, “beneath”, “lower”, “above”, “upper”, etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as “below” or “beneath” another component will be placed “above” the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.
- unit or “module” used in the specification means a hardware component such as software, FPGA or ASIC, and "unit” or “module” performs certain roles. However, “unit” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and “units” or “modules” may be combined into fewer components and “units” or “modules” or may be combined into additional components and “units” or “modules”. can be further separated.
- medical image data is a medical image captured by a medical imaging equipment, and includes all medical images that can be implemented as a 2D model or a 3D model of a subject's body.
- Medical image data may include X-ray, Computed Tomography (CT) images, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) images, etc. can
- FIG. 1 is a diagram for a brief description of ventricular-peritoneal shunting
- FIG. 2 is a diagram for explaining an x-ray image as medical image data of a patient who has undergone ventricular-peritoneal shunting and its structure.
- the human brain is a soft structure like the head, and the skull surrounding it is composed of hard bones.
- a fluid called cerebrospinal fluid surrounds the brain and acts as a buffer against direct shock from the skull. Without the cushioning material surrounding the brain, our brain would hit the skull every time the body was shaken and would not survive.
- About 150cc to 200cc of cerebrospinal fluid is contained around our brain, and about 500ml of cerebrospinal fluid is produced, absorbed, and circulated per day. In normal conditions, the production and absorption of cerebrospinal fluid is balanced, but when hydrocephalus occurs, this balance is broken, and the unabsorbed cerebrospinal fluid increases intracranial pressure, resulting in various symptoms.
- the most effective surgical treatment for treating hydrocephalus is to insert a flexible tube called a shunt into the patient's cerebrospinal fluid passage to allow the cerebrospinal fluid to be absorbed from other parts of the body (mainly the abdominal cavity, thoracic cavity, or atrium).
- a first catheter 10 is inserted into the patient's ventricle and a second catheter 20 is inserted into the patient's abdominal cavity so that cerebrospinal fluid is drained into the patient's abdominal cavity.
- a pressure control valve 30 is provided between the first catheter 10 and the second catheter 20 to adjust the amount of drainage of the cerebrospinal fluid.
- FIG. 3 is a block diagram showing the configuration of a pressure calculation system for ventricular-peritoneal shunting according to an embodiment of the present invention.
- a pressure calculating system may include a pressure calculating device 100 and at least one manager terminal 200 .
- the pressure calculating device 100 may include a communication module 110, a learning module 130, a storage module 150, and a control module 170.
- the communication module 110 transmits and receives at least one piece of information or data with at least one manager terminal 200 .
- the communication module 110 may perform communication with other servers or devices, and transmit and receive wireless signals in a communication network according to wireless Internet technologies.
- Wireless Internet technologies include, for example, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), etc., and the pressure calculating device (100 ) transmits and receives data according to at least one wireless Internet technology within a range including Internet technologies not listed above.
- BluetoothTM RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, NFC (Near Field Communication), Wi -Local communication may be supported using at least one of wireless-fidelity (Fi), Wi-Fi Direct, and wireless universal serial bus (USB) technologies.
- Wireless communication between the pressure calculating device 100 and at least one manager terminal 200 may be supported through such wireless area networks.
- the local area wireless communication network may be a local area wireless personal area network (Wireless Personal Area Networks).
- the learning module 130 learns an artificial intelligence model using an input data set.
- the artificial intelligence model may be at least one, but is not limited thereto.
- the storage module 150 stores various data (information), artificial intelligence models, etc. received by the pressure calculating device 100 or generated or acquired (measured) by the pressure calculating device 100, and stores the patient's medical image data. Stores at least one process required to calculate the corresponding pressure value.
- at least one artificial intelligence model may be stored.
- the control module 170 controls all components to perform an operation for calculating a pressure value corresponding to the patient's medical image data based on at least one process stored in the storage module 150.
- control module 170 collects a plurality of medical image data, checks the pressure value labeled on each of the collected plurality of medical image data, generates a data set, and then uses the generated data set. Perform training on artificial intelligence models.
- the plurality of medical image data is an X-ray image of a patient who has undergone ventricular-abdominal shunting and discharges cerebrospinal fluid, and may include medical image data determined to be in normal operation according to preset standards.
- each of the plurality of medical image data may further label or include body shape information of the patient in addition to the pressure value.
- the control module 170 may further consider the body shape information when generating the data set.
- the body shape information may include information on at least one of age, height, and weight that affect the amount of cerebrospinal fluid drainage. In this way, when using a data set in which body shape information that affects the displacement of cerebrospinal fluid is further considered, a pressure value more suitable for the patient can be calculated.
- control module 170 calculates and provides a pressure value corresponding to the input medical image data using the pre-learned artificial intelligence model.
- control module 170 may manually receive the pressure value of the pressure control valve for each of the plurality of medical image data when generating the data set, but the pressure control valve for each of the plurality of collected medical image data. may be detected, the detected pressure values of the pressure control valve may be checked, and the checked pressure values may be automatically labeled in corresponding medical image data.
- the pressure calculating device 100 is attached to the pressure control valve 20 provided between the first catheter inserted into the patient's ventricle and the second catheter inserted into the patient's abdominal cavity after the calculated pressure value is applied.
- the pressure calculating device 100 is attached to the pressure control valve 20 provided between the first catheter inserted into the patient's ventricle and the second catheter inserted into the patient's abdominal cavity after the calculated pressure value is applied.
- At least one manager terminal 200 may be a terminal used by a manager, and displays notification information received from the pressure calculating device 100 on a display unit so that the manager checks the patient's condition.
- the manager may be a medical staff, a caregiver, a guardian, and the like, but is not limited thereto.
- the notification information may include at least one of patient information, message information informing of an abnormality, measurement value information for measuring an actual culture medium (a measurement value determined to be abnormal), and an adjustment value to be adjusted to lower or increase the pressure.
- the notification information may further include measurement value information measured prior to the point in time at which the abnormality is detected so as to check the occurrence trend of the abnormality.
- the measurement value measured before that time may be all data or data within a preset range, and may be, for example, a measurement value measured right before the measurement value determined to be abnormal.
- At least one administrator terminal 200 is a mobile terminal, mobile phone, smart phone, laptop computer, desktop computer, digital broadcasting terminal, PDA (personal digital assistants), PMP, respectively.
- PDA personal digital assistants
- PMP portable multimedia player
- navigation slate PC, tablet PC, ultrabook
- wearable device e.g., watch type terminal (smartwatch), glass type terminal (smart glass), a computer device such as a head mounted display (HMD) or an electronic communication device, but is not limited thereto.
- HMD head mounted display
- FIG. 4 is a diagram showing an example of a pressure gauge indicator for checking a pressure value in a pressure calculating device according to an embodiment of the present invention.
- a pressure control valve 30 is provided between the first catheter 10 inserted into the ventricle and the second catheter 20 inserted into the abdominal cavity of the patient. 30 is provided with a pressure gauge indicator 31 for displaying a pressure value.
- a sensor capable of measuring (collecting) the drainage amount of the cerebrospinal fluid drained based on the pressure value may be further provided.
- the sensor transmits the measured data, that is, data on the actual displacement amount, to the pressure calculating device 10 based on the Internet of Things (IoT).
- IoT Internet of Things
- FIG. 5 is a flow chart showing a pressure calculation method for ventricle-peritoneal shunting according to an embodiment of the present invention.
- a pressure value labeled on each of the collected plurality of medical image data is checked to create a data set (S303).
- each of the plurality of collected medical image data may be further labeled or include information on the body shape of the patient in addition to the pressure value, and the body shape information may be further considered when generating a dataset in step S303.
- the body shape information may include information on at least one of age, height, and weight that affect the amount of cerebrospinal fluid drainage.
- step S303 the pressure value for each of the plurality of medical image data may be labeled in advance, but the pressure control valve 30 is detected in each of the plurality of medical image data, and the detected pressure control valve 30 By checking the pressure value based on the pressure gauge indicator 31 of , the corresponding medical image data may be labeled. At this time, labeling can also be done automatically through a pre-learned artificial intelligence model.
- the artificial intelligence model is learned using the generated dataset (S305), and then, medical image data of a specific patient is input to the learned artificial intelligence model (S307).
- FIG. 6 is a diagram illustrating an example in which notification information transmitted from a pressure calculating device according to an embodiment of the present invention is displayed through a display unit of a manager terminal.
- the description will be limited to the first manager terminal 210 .
- the first manager terminal 210 displays it through the display unit 211 .
- the notification information may include basic information (name, gender, age, etc.), surgery date, etc. of the patient, as well as message information notifying abnormality, measurement value information obtained by measuring the actual culture medium, and the pressure. At least one of adjustment values to be adjusted to lower or increase may be further included.
- Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof.
- a software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.
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Abstract
The present invention relates to a pressure calculation method and apparatus for ventriculoperitoneal shunting. A pressure calculation method for ventriculoperitoneal shunting, according to an embodiment of the present invention, comprises the steps of: collecting a plurality of pieces of medical image data; generating a data set by confirming a pressure value labeled for each of the collected plurality of pieces of medical image data; training an artificial intelligence model by using the generated data set; and, if the medical image data is input, calculating a pressure value corresponding to input medical image data, by using the trained artificial intelligence model, wherein the pressure value is used to determine the drainage amount of cerebrospinal fluid drained from a first catheter inserted into a cerebral ventricle to a second catheter inserted into the peritoneal cavity, and can be applied to a pressure control valve provided between the first catheter and the second catheter.
Description
본 발명은 뇌실-복강 단락술을 위한 압력 산출 방법 및 장치에 관한 것으로, 보다 상세하게는 복수의 의료 영상을 기반으로 뇌실-복강 단락술을 위한 압력을 자동으로 산출하여 제공할 수 있는 뇌실-복강 단락술을 위한 압력 산출 방법 및 장치에 관한 것이다.The present invention relates to a method and apparatus for calculating pressure for ventricle-abdominal shunting, and more particularly, to a ventricle-abdominal shunting method capable of automatically calculating and providing pressure for ventricle-abdominal shunting based on a plurality of medical images. It relates to a pressure calculation method and device for
뇌와 척수를 감싸고 있거나 뇌실 내에 있는 뇌척수액(cerebrospinal fluid)의 양은 약 140ml 정도 되며, 뇌실 내에 약 25ml, 정상 뇌척수액 압력은 150mmH2O 이하이며, 분당 0.35ml씩 하루에 400~500ml 생산되고 있다. 뇌척수액의 흡수는 경막동(dural venous sinus)의 지주막 융모(arachnoid villi)를 통하여 지주막하 압력과 경막동 사이의 압력차 약 7mmH2O에 의하여 이루어진다. 수두증은 뇌척수액의 과다 생성, 뇌척수액로의 폐쇄, 정맥혈 배출장애 등의 원인에 의하여 발생한다. The amount of cerebrospinal fluid (cerebrospinal fluid) surrounding the brain and spinal cord or in the ventricle is about 140 ml, about 25 ml in the ventricle, and the normal cerebrospinal fluid pressure is less than 150 mmH 2 O, and 400 to 500 ml per day at 0.35 ml per minute is produced. Absorption of cerebrospinal fluid is achieved by a pressure difference of about 7mmH 2 O between the subarachnoid pressure and the dural sinus through the arachnoid villi of the dural sinus (dural venous sinus). Hydrocephalus is caused by causes such as excessive production of cerebrospinal fluid, obstruction of cerebrospinal fluid, and impaired venous drainage.
일반적으로 뇌실-복강 단락술(Ventriculoperitoneal shunting)은 다양한 원인의 수두증(Hydrocephalus)을 치료하기 위해 시행되는 수술이다. 두개골을 천공한 후 뇌실을 천자하고 이곳으로부터 피하를 터널링(tunnelling)하여 복부에 별개의 절개를 이용하여 복강내로 션트 시스템(Shunt System)의 카테터(Catheter)를 삽입하는 수술이다. 이때, 뇌척수액이 뇌실에서 복강으로 배액되는 양을 조절하기 위해 뇌실 및 복강 간에 압력조절 밸브장치 또는 유량조절 밸브장치를 구비하여 뇌척수액의 배액량(배출량)을 조절하도록 한다. In general, ventriculoperitoneal shunting is an operation performed to treat various causes of hydrocephalus. It is a surgery to insert a catheter of a shunt system into the abdominal cavity using a separate incision in the abdomen by perforating the skull, puncturing the ventricles, and tunneling the subcutaneous tissue therefrom. At this time, in order to control the amount of cerebrospinal fluid drained from the ventricle to the abdominal cavity, a pressure control valve device or a flow control valve device is provided between the ventricle and the abdominal cavity to control the amount of discharged fluid (discharge amount) of the cerebrospinal fluid.
이 뇌척수액이 과다배액 될 경우에는 두통, 오심, 기립성 저혈압, 두개강내 출혈 등의 증상을 유발할 수 있고, 반대로 뇌척수액이 과소배액 될 경우에는 뇌압 상승 등의 증상을 유발할 수 있다.When this cerebrospinal fluid is overdrained, symptoms such as headache, nausea, orthostatic hypotension, and intracranial hemorrhage may be caused, and conversely, when cerebrospinal fluid is underdrained, symptoms such as increased intracranial pressure may be induced.
따라서, 각 환자에게 적합한 압력값을 제공 및 조절해주는 기술이 개발될 필요가 있다.Therefore, it is necessary to develop a technology for providing and adjusting a pressure value suitable for each patient.
본 발명은 상기한 바와 같은 문제점을 해결하기 위하여 제안된 것으로, 인공지능 모델을 기반으로 복수의 의료 영상 각각에 대응하는 입력값으로 구성된 데이터 셋을 학습하고, 그 학습 데이터를 이용하여 입력되는 의료 이미지에 적합한 입력값을 자동으로 산출하여 제공할 수 있도록 하는 뇌실-복강 단락술을 위한 압력 산출 방법 및 장치를 제공함에 있다.The present invention has been proposed to solve the above problems, and based on an artificial intelligence model, a data set composed of input values corresponding to a plurality of medical images is learned, and the input medical image is obtained using the learning data. It is to provide a pressure calculation method and device for ventricular-peritoneal shunting that can automatically calculate and provide an input value suitable for
또한, 본 발명은 사용자는 환자의 의료 영상을 육안으로 확인하고 경험치에 의존하여 입력값을 결정할 필요없이 다른 환자들에 대한 정상 동작 데이터들을 이용하여 신뢰성 높은 입력값을 자동으로 산출함으로써, 잘못된 압력값을 적용함에 따라 발생할 수 있는 증상 또는 합병증을 발생을 최소화할 수 있도록 하는 뇌실-복강 단락술을 위한 압력 산출 방법 및 장치를 제공함에 있다.In addition, the present invention automatically calculates a highly reliable input value using normal operation data of other patients without the need for the user to visually check the patient's medical image and determine the input value depending on experience, thereby erroneous pressure values. It is to provide a pressure calculation method and apparatus for ventricle-peritoneal shunting that can minimize the occurrence of symptoms or complications that may occur as a result of applying the.
본 발명이 해결하고자 하는 과제들은 이상에서 언급된 과제로 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 통상의 기술자에게 명확하게 이해될 수 있을 것이다.The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.
상술한 과제를 해결하기 위한 본 발명의 일 실시예에 따른 뇌실-복강 단락술을 위한 압력 산출 방법은, 복수개의 의료영상데이터를 수집하는 단계; 상기 수집된 복수개의 의료영상데이터 각각에 라벨링된 압력값을 확인하여 데이터셋을 생성하는 단계; 상기 생성된 데이터셋을 이용하여 인공지능 모델에 대한 학습을 수행하는 단계; 및 의료영상데이터가 입력되면, 상기 학습된 인공지능 모델을 이용하여 상기 입력된 의료영상데이터에 대응하는 압력값을 산출하는 단계를 포함하며, 상기 압력값은 뇌실에 삽입된 제1 카테터에서 복강에 삽입된 제2 카테터로 배액되는 뇌척수액의 배액량을 결정하는 것으로, 상기 제1 카테터와 상기 제2 카테터 사이에 구비된 압력조절밸브에 적용될 수 있다.A pressure calculation method for ventricle-peritoneal shunting according to an embodiment of the present invention for solving the above problems includes collecting a plurality of medical image data; generating a dataset by checking pressure values labeled on each of the plurality of collected medical image data; Performing learning on an artificial intelligence model using the generated dataset; and calculating a pressure value corresponding to the input medical image data using the learned artificial intelligence model when the medical image data is input, wherein the pressure value is transmitted from a first catheter inserted into the ventricle to the abdominal cavity. Determining the amount of cerebrospinal fluid drained through the inserted second catheter may be applied to a pressure control valve provided between the first catheter and the second catheter.
한편, 본 발명의 일 실시예에 따른 뇌실-복강 단락술을 위한 압력 산출 장치는, 적어도 하나의 정보 또는 데이터를 송수신하는 통신모듈; 데이터셋을 이용하여 인공지능 모델을 학습하는 학습모듈; 각종 정보 또는 데이터를 저장하고, 환자의 의료영상데이터에 대응하는 압력값을 산출하기 위해 필요한 적어도 하나의 프로세스를 저장하는 저장모듈; 및 상기 적어도 하나의 프로세스를 기반으로, 상기 환자의 의료영상데이터에 대응하는 압력값을 산출하기 위한 동작을 수행하는 제어모듈을 포함하며, 상기 제어모듈은, 복수개의 의료영상데이터를 수집하고, 상기 수집된 복수개의 의료영상데이터 각각에 라벨링된 압력값을 확인하여 데이터셋을 생성하고, 상기 생성된 데이터셋을 이용하여 인공지능 모델에 대한 학습을 수행한 후, 의료영상데이터가 입력되면, 상기 학습된 인공지능 모델을 이용하여 상기 입력된 의료영상데이터에 대응하는 압력값을 산출하며, 상기 압력값은 뇌실에 삽입된 제1 카테터에서 복강에 삽입된 제2 카테터로 배액되는 뇌척수액의 배액량을 결정하는 것으로, 상기 제1 카테터와 상기 제2 카테터 사이에 구비된 압력조절밸브에 적용될 수 있다.On the other hand, a pressure calculating device for ventricular-peritoneal shunting according to an embodiment of the present invention includes a communication module for transmitting and receiving at least one piece of information or data; A learning module for learning an artificial intelligence model using a dataset; a storage module for storing various types of information or data and storing at least one process required to calculate a pressure value corresponding to the patient's medical image data; and a control module that performs an operation for calculating a pressure value corresponding to the medical image data of the patient based on the at least one process, wherein the control module collects a plurality of medical image data, A data set is created by checking the pressure value labeled on each of the plurality of collected medical image data, and learning is performed on the artificial intelligence model using the generated data set. After the medical image data is input, the learning is performed. A pressure value corresponding to the input medical image data is calculated using the artificial intelligence model, and the pressure value determines the amount of cerebrospinal fluid drained from the first catheter inserted into the ventricle to the second catheter inserted into the abdominal cavity. As such, it can be applied to a pressure control valve provided between the first catheter and the second catheter.
본 발명의 기타 구체적인 사항들은 상세한 설명 및 도면들에 포함되어 있다.Other specific details of the invention are included in the detailed description and drawings.
본 발명에 의하면, 인공지능 모델을 기반으로 복수의 의료 영상 각각에 대응하는 입력값으로 구성된 데이터셋을 학습하고, 그 학습 데이터를 이용하여 입력되는 환자의 의료 영상에 적합한 입력값을 자동으로 산출하여 제공할 수 있도록 한다.According to the present invention, a dataset composed of input values corresponding to each of a plurality of medical images is learned based on an artificial intelligence model, and input values suitable for the patient's medical images are automatically calculated using the learning data. to be able to provide
또한, 본 발명에 의하면, 사용자는 환자의 의료 영상을 육안으로 확인하고 경험치에 의존하여 입력값을 결정할 필요없이 다른 환자들에 대한 정상 동작 데이터들을 이용하여 신뢰성 높은 입력값을 자동으로 산출함으로써, 잘못된 압력값을 적용함에 따라 발생할 수 있는 증상 또는 합병증을 발생을 최소화할 수 있도록 한다.In addition, according to the present invention, the user does not have to visually check the patient's medical image and determine the input value depending on experience, but automatically calculates a highly reliable input value using normal operation data of other patients, Minimize the occurrence of symptoms or complications that may occur by applying the pressure value.
본 발명의 효과들은 이상에서 언급된 효과로 제한되지 않으며, 언급되지 않은 또 다른 효과들은 아래의 기재로부터 통상의 기술자에게 명확하게 이해될 수 있을 것이다.The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.
도 1은 뇌실-복강 단락술에 대한 대략적인 설명을 위한 도면이다.1 is a diagram for a brief description of ventricle-peritoneal shunting.
도 2는 뇌실-복강 단락술을 시술한 환자의 의료영상데이터로서 x-ray 영상 및 그 시술된 구성을 설명하기 위한 도면이다.FIG. 2 is a view for explaining an x-ray image as medical image data of a patient who has undergone ventricular-abdominal cavity shunting and its structure.
도 3은 본 발명의 실시예에 따른 뇌실-복강 단락술을 위한 압력 산출 시스템의 구성을 나타내는 블록도이다.3 is a block diagram showing the configuration of a pressure calculation system for ventricular-peritoneal shunting according to an embodiment of the present invention.
도 4는 본 발명의 실시예에 따른 압력 산출 장치에서 압력값을 확인하기 위한 압력 게이지 인디케이터의 일 예를 도시한 도면이다.4 is a diagram showing an example of a pressure gauge indicator for checking a pressure value in a pressure calculating device according to an embodiment of the present invention.
도 5는 본 발명의 실시예에 따른 뇌실-복강 단락술을 위한 압력 산출 방법을 나타내는 순서도이다.5 is a flow chart showing a pressure calculation method for ventricle-peritoneal shunting according to an embodiment of the present invention.
도 6은 본 발명의 일 실시예에 따른 압력 산출 장치에서 송신한 알림정보가 관리자 단말의 디스플레이부를 통해 디스플레이된 일 예를 나타내는 도면이다.6 is a diagram illustrating an example in which notification information transmitted from a pressure calculating device according to an embodiment of the present invention is displayed through a display unit of a manager terminal.
본 발명의 이점 및 특징, 그리고 그것들을 달성하는 방법은 첨부되는 도면과 함께 상세하게 후술되어 있는 실시예들을 참조하면 명확해질 것이다. 그러나, 본 발명은 이하에서 개시되는 실시예들에 제한되는 것이 아니라 서로 다른 다양한 형태로 구현될 수 있으며, 단지 본 실시예들은 본 발명의 개시가 완전하도록 하고, 본 발명이 속하는 기술 분야의 통상의 기술자에게 본 발명의 범주를 완전하게 알려주기 위해 제공되는 것이며, 본 발명은 청구항의 범주에 의해 정의될 뿐이다. Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.
본 명세서에서 사용된 용어는 실시예들을 설명하기 위한 것이며 본 발명을 제한하고자 하는 것은 아니다. 본 명세서에서, 단수형은 문구에서 특별히 언급하지 않는 한 복수형도 포함한다. 명세서에서 사용되는 "포함한다(comprises)" 및/또는 "포함하는(comprising)"은 언급된 구성요소 외에 하나 이상의 다른 구성요소의 존재 또는 추가를 배제하지 않는다. 명세서 전체에 걸쳐 동일한 도면 부호는 동일한 구성 요소를 지칭하며, "및/또는"은 언급된 구성요소들의 각각 및 하나 이상의 모든 조합을 포함한다. 비록 "제1", "제2" 등이 다양한 구성요소들을 서술하기 위해서 사용되나, 이들 구성요소들은 이들 용어에 의해 제한되지 않음은 물론이다. 이들 용어들은 단지 하나의 구성요소를 다른 구성요소와 구별하기 위하여 사용하는 것이다. 따라서, 이하에서 언급되는 제1 구성요소는 본 발명의 기술적 사상 내에서 제2 구성요소일 수도 있음은 물론이다.Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.
다른 정의가 없다면, 본 명세서에서 사용되는 모든 용어(기술 및 과학적 용어를 포함)는 본 발명이 속하는 기술분야의 통상의 기술자에게 공통적으로 이해될 수 있는 의미로 사용될 수 있을 것이다. 또한, 일반적으로 사용되는 사전에 정의되어 있는 용어들은 명백하게 특별히 정의되어 있지 않는 한 이상적으로 또는 과도하게 해석되지 않는다.Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.
공간적으로 상대적인 용어인 "아래(below)", "아래(beneath)", "하부(lower)", "위(above)", "상부(upper)" 등은 도면에 도시되어 있는 바와 같이 하나의 구성요소와 다른 구성요소들과의 상관관계를 용이하게 기술하기 위해 사용될 수 있다. 공간적으로 상대적인 용어는 도면에 도시되어 있는 방향에 더하여 사용시 또는 동작시 구성요소들의 서로 다른 방향을 포함하는 용어로 이해되어야 한다. 예를 들어, 도면에 도시되어 있는 구성요소를 뒤집을 경우, 다른 구성요소의 "아래(below)"또는 "아래(beneath)"로 기술된 구성요소는 다른 구성요소의 "위(above)"에 놓여질 수 있다. 따라서, 예시적인 용어인 "아래"는 아래와 위의 방향을 모두 포함할 수 있다. 구성요소는 다른 방향으로도 배향될 수 있으며, 이에 따라 공간적으로 상대적인 용어들은 배향에 따라 해석될 수 있다.The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.
명세서에서 사용되는 "부" 또는 "모듈"이라는 용어는 소프트웨어, FPGA 또는 ASIC과 같은 하드웨어 구성요소를 의미하며, "부" 또는 "모듈"은 어떤 역할들을 수행한다. 그렇지만 "부" 또는 "모듈"은 소프트웨어 또는 하드웨어에 한정되는 의미는 아니다. "부" 또는 "모듈"은 어드레싱할 수 있는 저장 매체에 있도록 구성될 수도 있고 하나 또는 그 이상의 프로세서들을 재생시키도록 구성될 수도 있다. 따라서, 일 예로서 "부" 또는 "모듈"은 소프트웨어 구성요소들, 객체지향 소프트웨어 구성요소들, 클래스 구성요소들 및 태스크 구성요소들과 같은 구성요소들과, 프로세스들, 함수들, 속성들, 프로시저들, 서브루틴들, 프로그램 코드의 세그먼트들, 드라이버들, 펌웨어, 마이크로 코드, 회로, 데이터, 데이터베이스, 데이터 구조들, 테이블들, 어레이들 및 변수들을 포함한다. 구성요소들과 "부" 또는 "모듈"들 안에서 제공되는 기능은 더 작은 수의 구성요소들 및 "부" 또는 "모듈"들로 결합되거나 추가적인 구성요소들과 "부" 또는 "모듈"들로 더 분리될 수 있다.The term "unit" or "module" used in the specification means a hardware component such as software, FPGA or ASIC, and "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. A "unit" or "module" may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and "units" or "modules" may be combined into fewer components and "units" or "modules" or may be combined into additional components and "units" or "modules". can be further separated.
다른 정의가 없다면, 본 명세서에서 사용되는 모든 용어(기술 및 과학적 용어를 포함)는 본 발명이 속하는 기술분야의 통상의 기술자에게 공통적으로 이해될 수 있는 의미로 사용될 수 있을 것이다. 또한, 일반적으로 사용되는 사전에 정의되어 있는 용어들은 명백하게 특별히 정의되어 있지 않는 한 이상적으로 또는 과도하게 해석되지 않는다.Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.
본 명세서에서 "의료영상데이터"는 의료영상 촬영장비로 촬영되는 의료영상으로서, 대상체의 신체를 2차원 모델 또는 3차원 모델로 구현 가능한 모든 의료영상을 포함한다. "의료영상데이터"는 엑스레이(X-ray), 컴퓨터 단층촬영(Computed Tomography;CT)영상, 자기공명영상(Magnetic Resonance Imaging; MRI), 양전자 단층촬영(Positron Emission Tomography; PET) 영상 등을 포함할 수 있다.In this specification, "medical image data" is a medical image captured by a medical imaging equipment, and includes all medical images that can be implemented as a 2D model or a 3D model of a subject's body. "Medical image data" may include X-ray, Computed Tomography (CT) images, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) images, etc. can
이하, 첨부된 도면을 참조하여 본 발명의 실시예를 상세하게 설명한다.Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
도 1은 뇌실-복강 단락술에 대한 대략적인 설명을 위한 도면이고, 도 2는 뇌실-복강 단락술을 시술한 환자의 의료영상데이터로서 x-ray 영상 및 그 시술된 구성을 설명하기 위한 도면이다.FIG. 1 is a diagram for a brief description of ventricular-peritoneal shunting, and FIG. 2 is a diagram for explaining an x-ray image as medical image data of a patient who has undergone ventricular-peritoneal shunting and its structure.
사람의 뇌는 두부와 같이 부드러운 구조로 되어있고, 이를 둘러싼 두개골은 딱딱한 뼈로 구성된다. 뇌를 보호하기 위해 뇌척수액이라는 액체가 뇌 주위를 둘러싸고 있어 두개골 등으로부터의 직접적인 충격을 완화시켜주는 일을 한다. 뇌를 둘러싸고 있는 완충재가 없다면, 우리 뇌는 몸이 흔들릴 때마다 두개골에 부딪혀 남아나지 못하게 된다. 우리 뇌 주위에는 약 150cc 내지 200cc의 뇌척수액이 들어있고, 하루 약 500ml의 뇌척수액이 생성 및 흡수돼 순환된다. 정상적인 상태에서는 뇌척수액의 생성과 흡수에 균형이 이루어져 있지만, 수두증이 생기면 이런 균형이 깨지게 되고 흡수가 되지 않은 뇌척수액이 두개강 내 압력을 증가시켜 여러 가지 증세를 나타내게 된다. 이런 수두증 치료에 가장 효과적인 수술적 치료는 환자의 뇌척수액 통로에 션트(shunt)라는 유연한 튜브를 집어넣어 뇌척수액을 신체의 다른 부위(주로 복강, 흉강 또는 심방)에서 흡수되도록 만들어주는 것이다. The human brain is a soft structure like the head, and the skull surrounding it is composed of hard bones. To protect the brain, a fluid called cerebrospinal fluid surrounds the brain and acts as a buffer against direct shock from the skull. Without the cushioning material surrounding the brain, our brain would hit the skull every time the body was shaken and would not survive. About 150cc to 200cc of cerebrospinal fluid is contained around our brain, and about 500ml of cerebrospinal fluid is produced, absorbed, and circulated per day. In normal conditions, the production and absorption of cerebrospinal fluid is balanced, but when hydrocephalus occurs, this balance is broken, and the unabsorbed cerebrospinal fluid increases intracranial pressure, resulting in various symptoms. The most effective surgical treatment for treating hydrocephalus is to insert a flexible tube called a shunt into the patient's cerebrospinal fluid passage to allow the cerebrospinal fluid to be absorbed from other parts of the body (mainly the abdominal cavity, thoracic cavity, or atrium).
그 중 뇌실-복강 단락술은 환자의 뇌실에 제1 카테터(10)를 삽입하고, 환자의 복강에 제2 카테터(20)를 삽입하여 뇌척수액이 환자의 복강으로 배액되도록 한다. 이때, 제1 카테터(10) 및 제2 카테터(20) 사이에는 뇌척수약의 배액량을 조절할 수 있는 압력조절밸브(30)가 구비된다.Among them, in the ventricle-abdominal cavity shunting, a first catheter 10 is inserted into the patient's ventricle and a second catheter 20 is inserted into the patient's abdominal cavity so that cerebrospinal fluid is drained into the patient's abdominal cavity. At this time, a pressure control valve 30 is provided between the first catheter 10 and the second catheter 20 to adjust the amount of drainage of the cerebrospinal fluid.
도 3은 본 발명의 실시예에 따른 뇌실-복강 단락술을 위한 압력 산출 시스템의 구성을 나타내는 블록도이다.3 is a block diagram showing the configuration of a pressure calculation system for ventricular-peritoneal shunting according to an embodiment of the present invention.
도 3을 참조하면, 본 발명의 실시예에 따른 압력 산출 시스템은 압력 산출 장치(100) 및 적어도 하나의 관리자 단말(200)을 포함할 수 있다. Referring to FIG. 3 , a pressure calculating system according to an embodiment of the present invention may include a pressure calculating device 100 and at least one manager terminal 200 .
구체적으로, 압력 산출 장치(100)는 통신모듈(110), 학습모듈(130), 저장모듈(150) 및 제어모듈(170)을 포함하여 구성될 수 있다.Specifically, the pressure calculating device 100 may include a communication module 110, a learning module 130, a storage module 150, and a control module 170.
통신모듈(110)은 적어도 하나의 관리자 단말(200)과 적어도 하나의 정보 또는 데이터를 송수신한다. 또한, 이 통신모듈(110)은 그 외 다른 서버 또는 장치들과의 통신을 수행할 수도 있는 것으로, 무선 인터넷 기술들에 따른 통신망에서 무선 신호를 송수신하도록 한다.The communication module 110 transmits and receives at least one piece of information or data with at least one manager terminal 200 . In addition, the communication module 110 may perform communication with other servers or devices, and transmit and receive wireless signals in a communication network according to wireless Internet technologies.
무선 인터넷 기술로는, 예를 들어 WLAN(Wireless LAN), Wi-Fi(Wireless-Fidelity), Wi-Fi(Wireless Fidelity) Direct, DLNA(Digital Living Network Alliance), WiBro(Wireless Broadband), WiMAX(World Interoperability for Microwave Access), HSDPA(High Speed Downlink Packet Access), HSUPA(High Speed Uplink Packet Access), LTE(Long Term Evolution), LTE-A(Long Term Evolution-Advanced) 등이 있으며, 압력 산출 장치(100)는 앞에서 나열되지 않은 인터넷 기술까지 포함한 범위에서 적어도 하나의 무선 인터넷 기술에 따라 데이터를 송수신하게 된다.Wireless Internet technologies include, for example, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), etc., and the pressure calculating device (100 ) transmits and receives data according to at least one wireless Internet technology within a range including Internet technologies not listed above.
근거리 통신(Short range communication)을 위한 것으로서, 블루투스(Bluetooth™), RFID(Radio Frequency Identification), 적외선 통신(Infrared Data Association; IrDA), UWB(Ultra Wideband), ZigBee, NFC(Near Field Communication), Wi-Fi(Wireless-Fidelity), Wi-Fi Direct, Wireless USB(Wireless Universal Serial Bus) 기술 중 적어도 하나를 이용하여, 근거리 통신을 지원할 수 있다. 이러한, 근거리 무선 통신망(Wireless Area Networks)을 압력 산출 장치(100) 및 적어도 하나의 관리자 단말(200) 간 무선 통신을 지원할 수 있다. 이때, 근거리 무선 통신망은 근거리 무선 개인 통신망(Wireless Personal Area Networks)일 수 있다.As for short range communication, Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, NFC (Near Field Communication), Wi -Local communication may be supported using at least one of wireless-fidelity (Fi), Wi-Fi Direct, and wireless universal serial bus (USB) technologies. Wireless communication between the pressure calculating device 100 and at least one manager terminal 200 may be supported through such wireless area networks. At this time, the local area wireless communication network may be a local area wireless personal area network (Wireless Personal Area Networks).
학습모듈(130)은 입력되는 데이터셋을 이용하여 인공지능 모델을 학습한다. 이때, 인공지능 모델은 적어도 하나일 수 있으며, 이를 한정하지 않는다.The learning module 130 learns an artificial intelligence model using an input data set. At this time, the artificial intelligence model may be at least one, but is not limited thereto.
저장모듈(150)은 압력 산출 장치(100)로 수신되거나 압력 산출 장치(100)에 의해 생성 또는 획득(측정)되는 각종 데이터(정보), 인공지능 모델 등을 저장하고, 환자의 의료영상데이터에 대응하는 압력값을 산출하기 위해 필요한 적어도 하나의 프로세스를 저장한다. 여기서, 인공지능 모델은 적어도 하나 이상 저장될 수 있다.The storage module 150 stores various data (information), artificial intelligence models, etc. received by the pressure calculating device 100 or generated or acquired (measured) by the pressure calculating device 100, and stores the patient's medical image data. Stores at least one process required to calculate the corresponding pressure value. Here, at least one artificial intelligence model may be stored.
제어모듈(170)은 저장모듈(150)에 저장된 적어도 하나의 프로세스를 기반으로 모든 구성요소들을 통해 환자의 의료영상데이터에 대응하는 압력값을 산출하기 위한 동작을 수행하도록 제어한다.The control module 170 controls all components to perform an operation for calculating a pressure value corresponding to the patient's medical image data based on at least one process stored in the storage module 150.
구체적으로, 제어모듈(170)은 복수개의 의료영상데이터를 수집하고, 그 수집된 복수개의 의료영상데이터 각각에 라벨링된 압력값을 확인하여 데이터셋을 생성한 후, 그 생성된 데이터셋을 이용하여 인공지능 모델에 대한 학습을 수행한다. Specifically, the control module 170 collects a plurality of medical image data, checks the pressure value labeled on each of the collected plurality of medical image data, generates a data set, and then uses the generated data set. Perform training on artificial intelligence models.
한편, 복수개의 의료영상데이터는, 뇌실-복강 단락술이 실시되어 뇌척수액을 배출하는 환자의 엑스레이 촬영 이미지이며, 미리 설정된 기준에 따라 정상 동작으로 판단된 의료영상데이터들을 포함할 수 있다.Meanwhile, the plurality of medical image data is an X-ray image of a patient who has undergone ventricular-abdominal shunting and discharges cerebrospinal fluid, and may include medical image data determined to be in normal operation according to preset standards.
또한, 복수개의 의료영상데이터 각각은 압력값 외에도 환자에 대한 체형정보가 더 라벨링되거나 포함될 수 있는데, 이 경우 제어모듈(170)은 데이터셋을 생성할 시에 그 체형정보를 더 고려할 수 있다. 이때, 체형정보는 뇌척수액의 배액량에 영향을 주는 연령, 키 및 몸무게 중 적어도 하나에 대한 정보를 포함할 수 있다. 이와 같이 뇌척수액의 배약량에 영향을 주는 체형정보가 더 고려된 데이터셋을 이용할 경우, 해당 환자에 보다 적합한 압력값이 산출될 수 있다.In addition, each of the plurality of medical image data may further label or include body shape information of the patient in addition to the pressure value. In this case, the control module 170 may further consider the body shape information when generating the data set. In this case, the body shape information may include information on at least one of age, height, and weight that affect the amount of cerebrospinal fluid drainage. In this way, when using a data set in which body shape information that affects the displacement of cerebrospinal fluid is further considered, a pressure value more suitable for the patient can be calculated.
이후, 제어모듈(170)은 특정 환자의 의료영상데이터가 입력되면, 기학습된 인공지능 모델을 이용하여 그 입력된 의료영상데이터에 대응하는 압력값을 산출하여 제공한다. Then, when the medical image data of a specific patient is input, the control module 170 calculates and provides a pressure value corresponding to the input medical image data using the pre-learned artificial intelligence model.
이때, 제어모듈(170)은 데이터셋을 생성할 시, 복수개의 의료영상데이터 각각에 대한 압력조절밸브의 압력값을 수동으로 입력받을 수도 있으나, 수집된 복수개의 의료영상데이터 각각에 대해 압력조절밸브를 검출하고, 상기 검출된 압력조절밸브의 압력값들을 확인하여, 그 확인된 압력값을 해당 의료영상데이터에 자동 라벨링 할 수도 있다.At this time, the control module 170 may manually receive the pressure value of the pressure control valve for each of the plurality of medical image data when generating the data set, but the pressure control valve for each of the plurality of collected medical image data. may be detected, the detected pressure values of the pressure control valve may be checked, and the checked pressure values may be automatically labeled in corresponding medical image data.
한편, 압력 산출 장치(100)는 그 산출된 압력값이 적용된 이후, 환자의 뇌실에 삽입된 제1 카테터와 환자의 복강에 삽입된 제2 카테터 사이에 구비된 압력조절밸브(20)에 부착된 센서로부터 실제 배액량에 대한 데이터가 수신되면, 미리 설정된 주기 마다 미리 설정된 임계범위를 기반으로 상기 실제 배액량에 대한 이상 여부를 판단한다. 그 판단 결과, 이상이 있는 것으로 판단되면, 알림정보를 생성하여 미리 등록된 적어도 하나의 관리자 단말(200)로 송신한다. On the other hand, the pressure calculating device 100 is attached to the pressure control valve 20 provided between the first catheter inserted into the patient's ventricle and the second catheter inserted into the patient's abdominal cavity after the calculated pressure value is applied. When data on the actual drainage amount is received from the sensor, it is determined whether or not the actual drainage amount is abnormal based on a preset threshold range at each preset period. As a result of the determination, if it is determined that there is an abnormality, notification information is generated and transmitted to at least one manager terminal 200 registered in advance.
한편, 적어도 하나의 관리자 단말(200)은 관리자가 사용하는 단말일 수 있으며, 압력 산출 장치(100)로부터 수신되는 알림정보를 디스플레이부에 디스플레이함으로써, 관리자가 환자의 상태를 확인하도록 한다. 여기서, 관리자는 의료진, 간병인, 보호자 등이 될 수 있으며, 이를 한정하지는 않는다. Meanwhile, at least one manager terminal 200 may be a terminal used by a manager, and displays notification information received from the pressure calculating device 100 on a display unit so that the manager checks the patient's condition. Here, the manager may be a medical staff, a caregiver, a guardian, and the like, but is not limited thereto.
한편, 알림정보는 환자 정보, 이상을 알리는 메시지 정보, 실제 배양액을 측정한 측정값 정보(이상이 있는 것으로 판단한 측정값) 및 그 압력을 낮추거나 높이기 위해 조절해야 하는 조절값 중 적어도 하나를 포함할 수 있다. 또한, 알림정보는 이상 발생 추이를 확인할 수 있도록 이상이 감지된 시점을 기준으로 그 전에 측정된 측정값 정보를 더 포함할 수 있다. 이때, 그 전에 측정된 측정값은 전체 또는 미리 설정된 범위 이내의 데이터일 수 있으며, 예를 들어, 이상이 있는 것으로 판단한 측정값 바로 직전에 측정한 측정값일 수 있다.On the other hand, the notification information may include at least one of patient information, message information informing of an abnormality, measurement value information for measuring an actual culture medium (a measurement value determined to be abnormal), and an adjustment value to be adjusted to lower or increase the pressure. can In addition, the notification information may further include measurement value information measured prior to the point in time at which the abnormality is detected so as to check the occurrence trend of the abnormality. In this case, the measurement value measured before that time may be all data or data within a preset range, and may be, for example, a measurement value measured right before the measurement value determined to be abnormal.
아울러, 적어도 하나의 관리자 단말(200)은 각각 이동 단말기, 휴대폰, 스마트 폰(smart phone), 노트북 컴퓨터(laptop computer), 데스크탑 컴퓨터(desktop computer), 디지털방송용 단말기, PDA(personal digital assistants), PMP(portable multimedia player), 네비게이션, 슬레이트 PC(slate PC), 태블릿 PC(tablet PC), 울트라 북(ultrabook), 웨어러블 디바이스(wearable device, 예를 들어, 워치형 단말기 (smartwatch), 글래스형 단말기 (smart glass), HMD(head mounted display) 등과 같은 컴퓨터 장치 또는 전기 통신 장치일 수 있으나, 이에 한정되는 것은 아니다.In addition, at least one administrator terminal 200 is a mobile terminal, mobile phone, smart phone, laptop computer, desktop computer, digital broadcasting terminal, PDA (personal digital assistants), PMP, respectively. (portable multimedia player), navigation, slate PC, tablet PC, ultrabook, wearable device (e.g., watch type terminal (smartwatch), glass type terminal (smart glass), a computer device such as a head mounted display (HMD) or an electronic communication device, but is not limited thereto.
도 4는 본 발명의 실시예에 따른 압력 산출 장치에서 압력값을 확인하기 위한 압력 게이지 인디케이터의 일 예를 도시한 도면이다.4 is a diagram showing an example of a pressure gauge indicator for checking a pressure value in a pressure calculating device according to an embodiment of the present invention.
도 4를 참조하면, 앞서 설명한 바와 같이, 뇌실에 삽입된 제1 카테터(10)와 환자의 복강에 삽입된 제2 카테터(20) 사이에는 압력조절밸브(30)가 구비되는데, 그 압력조절밸브(30)에는 압력값을 표시하는 압력 게이지 인디케이터(31)가 구비된다.Referring to FIG. 4, as described above, a pressure control valve 30 is provided between the first catheter 10 inserted into the ventricle and the second catheter 20 inserted into the abdominal cavity of the patient. 30 is provided with a pressure gauge indicator 31 for displaying a pressure value.
한편, 도 4에는 도시하지 않았으나, 그 압력값을 기반으로 배액되는 뇌척수액의 배액량을 측정(수집)할 수 있는 센서가 더 구비될 수 있다. 이때, 센서는 사물인터넷(Internet of Things, IoT)을 기반으로 그 측정된 데이터, 즉, 실제 배액량에 대한 데이터를 압력 산출 장치(10)로 송신한다.Meanwhile, although not shown in FIG. 4 , a sensor capable of measuring (collecting) the drainage amount of the cerebrospinal fluid drained based on the pressure value may be further provided. At this time, the sensor transmits the measured data, that is, data on the actual displacement amount, to the pressure calculating device 10 based on the Internet of Things (IoT).
도 5는 본 발명의 실시예에 따른 뇌실-복강 단락술을 위한 압력 산출 방법을 나타내는 순서도이다.5 is a flow chart showing a pressure calculation method for ventricle-peritoneal shunting according to an embodiment of the present invention.
도 5를 참조하면, 복수개의 의료영상데이터를 수집 또는 입력되면(S301), 그 수집된 복수개의 의료영상데이터 각각에 라벨링된 압력값을 확인하여 데이터셋을 생성한다(S303). Referring to FIG. 5 , when a plurality of medical image data is collected or input (S301), a pressure value labeled on each of the collected plurality of medical image data is checked to create a data set (S303).
여기서, 수집된 복수개의 의료영상데이터 각각은 압력값 외에도 환자에 대한 체형정보가 더 라벨링되거나 포함할 수 있으며, S303 단계에서 데이터셋을 생성할 시에 그 체형정보를 더 고려할 수 있다. 이때, 체형정보는 뇌척수액의 배액량에 영향을 주는 연령, 키 및 몸무게 중 적어도 하나에 대한 정보를 포함할 수 있다. Here, each of the plurality of collected medical image data may be further labeled or include information on the body shape of the patient in addition to the pressure value, and the body shape information may be further considered when generating a dataset in step S303. In this case, the body shape information may include information on at least one of age, height, and weight that affect the amount of cerebrospinal fluid drainage.
한편, S303 단계에서 복수개의 의료영상데이터 각각에 대한 압력값은 사전에 라벨링되어 있을 수도 있지만, 복수개의 의료영상데이터 각각에서 압력조절밸브(30)를 검출하고, 그 검출된 압력조절밸브(30)의 압력 게이지 인디케이터(31)를 기반으로 압력값을 확인함으로써 해당 의료영상데이터에 라벨링할 수도 있다. 이때, 라벨링 또한 기학습된 인공지능 모델을 통해 자동으로 이뤄질 수 있다.Meanwhile, in step S303, the pressure value for each of the plurality of medical image data may be labeled in advance, but the pressure control valve 30 is detected in each of the plurality of medical image data, and the detected pressure control valve 30 By checking the pressure value based on the pressure gauge indicator 31 of , the corresponding medical image data may be labeled. At this time, labeling can also be done automatically through a pre-learned artificial intelligence model.
그 다음으로, 그 생성된 데이터셋을 이용하여 인공지능 모델에 대한 학습을 수행하고(S305), 이후, 그 학습된 인공지능 모델에 특정 환자의 의료영상데이터를 입력한다(S307).Next, the artificial intelligence model is learned using the generated dataset (S305), and then, medical image data of a specific patient is input to the learned artificial intelligence model (S307).
그 결과로서, 입력된 의료영상데이터에 해당하는 압력값이 산출된다(S309).As a result, a pressure value corresponding to the input medical image data is calculated (S309).
도 6은 본 발명의 일 실시예에 따른 압력 산출 장치에서 송신한 알림정보가 관리자 단말의 디스플레이부를 통해 디스플레이된 일 예를 나타내는 도면이다. 다만, 설명의 편의를 위하여 제1 관리자 단말(210)로 한정하여 설명하도록 한다.6 is a diagram illustrating an example in which notification information transmitted from a pressure calculating device according to an embodiment of the present invention is displayed through a display unit of a manager terminal. However, for convenience of description, the description will be limited to the first manager terminal 210 .
도 6을 참조하면, 제1 관리자 단말(210)은 압력 산출 장치(100)로부터 알림정보가 수신되면, 이를 디스플레이부(211)를 통해 디스플레이 한다.Referring to FIG. 6 , when notification information is received from the pressure calculating device 100 , the first manager terminal 210 displays it through the display unit 211 .
이때, 알림정보는 그 환자에 대한 기본정보(이름, 성별, 나이 등), 수술일자등을 포함할 수 있을 뿐만 아니라, 이상을 알리는 메시지 정보, 상기 실제 배양액을 측정한 측정값 정보 및 상기 압력을 낮추거나 높이기 위해 조절해야 하는 조절값 중 적어도 하나를 더 포함할 수 있다. At this time, the notification information may include basic information (name, gender, age, etc.), surgery date, etc. of the patient, as well as message information notifying abnormality, measurement value information obtained by measuring the actual culture medium, and the pressure. At least one of adjustment values to be adjusted to lower or increase may be further included.
본 발명의 실시예와 관련하여 설명된 방법 또는 알고리즘의 단계들은 하드웨어로 직접 구현되거나, 하드웨어에 의해 실행되는 소프트웨어 모듈로 구현되거나, 또는 이들의 결합에 의해 구현될 수 있다. 소프트웨어 모듈은 RAM(Random Access Memory), ROM(Read Only Memory), EPROM(Erasable Programmable ROM), EEPROM(Electrically Erasable Programmable ROM), 플래시 메모리(Flash Memory), 하드 디스크, 착탈형 디스크, CD-ROM, 또는 본 발명이 속하는 기술 분야에서 잘 알려진 임의의 형태의 컴퓨터 판독가능 기록매체에 상주할 수도 있다.Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.
이상, 첨부된 도면을 참조로 하여 본 발명의 실시예를 설명하였지만, 본 발명이 속하는 기술분야의 통상의 기술자는 본 발명이 그 기술적 사상이나 필수적인 특징을 변경하지 않고서 다른 구체적인 형태로 실시될 수 있다는 것을 이해할 수 있을 것이다. 그러므로, 이상에서 기술한 실시예들은 모든 면에서 예시적인 것이며, 제한적이 아닌 것으로 이해해야만 한다.Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.
Claims (15)
- 장치에 의해 수행되는, 뇌실-복강 단락술을 위한 압력 산출 방법에 있어서,In the pressure calculation method for ventricular-peritoneal shunting performed by the device,복수개의 의료영상데이터를 수집하는 단계;Collecting a plurality of medical image data;상기 수집된 복수개의 의료영상데이터 각각에 라벨링된 압력값을 확인하여 데이터셋을 생성하는 단계;generating a dataset by checking pressure values labeled on each of the plurality of collected medical image data;상기 생성된 데이터셋을 이용하여 인공지능 모델에 대한 학습을 수행하는 단계; 및Performing learning on an artificial intelligence model using the generated dataset; and의료영상데이터가 입력되면, 상기 학습된 인공지능 모델을 이용하여 상기 입력된 의료영상데이터에 대응하는 압력값을 산출하는 단계를 포함하며,When medical image data is input, calculating a pressure value corresponding to the input medical image data using the learned artificial intelligence model,상기 압력값은, 뇌실에 삽입된 제1 카테터에서 복강에 삽입된 제2 카테터로 배액되는 뇌척수액의 배액량을 결정하는 것으로, 상기 제1 카테터 및 상기 제2 카테터 사이에 구비된 압력조절밸브에 적용되는 것을 특징으로 하는,The pressure value determines the drainage amount of cerebrospinal fluid drained from the first catheter inserted into the ventricle to the second catheter inserted into the abdominal cavity, and is applied to a pressure control valve provided between the first catheter and the second catheter characterized in that,뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 제1항에 있어서,According to claim 1,상기 복수개의 의료영상데이터는,The plurality of medical image data,뇌실-복강 단락술이 실시되어 뇌척수액을 배출하는 환자의 엑스레이 촬영 이미지이며,It is an X-ray image of a patient who has undergone ventricular-abdominal shunting and discharged cerebrospinal fluid,미리 설정된 기준에 따라 정상 동작으로 판단된 의료영상데이터들을 포함하는 것을 특징으로 하는,Characterized in that it includes medical image data determined to be normal operation according to preset criteria,뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 제1항에 있어서,According to claim 1,상기 데이터셋을 생성하는 단계는,The step of creating the dataset is,상기 수집된 복수개의 의료영상데이터 각각에 대해 압력조절밸브를 검출하는 단계;detecting a pressure control valve for each of the plurality of collected medical image data;상기 검출된 압력조절밸브의 압력값들을 확인하는 단계; 및checking the pressure values of the detected pressure control valve; and상기 확인된 압력값을 해당 의료영상데이터에 라벨링하는 단계를 더 포함하는 것을 특징으로 하는,Characterized in that it further comprises the step of labeling the confirmed pressure value to the corresponding medical image data,뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 제1항에 있어서,According to claim 1,상기 검출된 압력조절밸브의 압력값은, 상기 검출된 압력조절밸브의 압력 게이지 인디케이터를 기반으로 확인하는 것을 특징으로 하는,Characterized in that the pressure value of the detected pressure control valve is confirmed based on the pressure gauge indicator of the detected pressure control valve.뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 제1항에 있어서,According to claim 1,상기 수집된 복수개의 의료영상데이터 각각은, 환자에 대한 체형정보를 포함하며, 상기 데이터셋을 생성할 시에 상기 체형정보를 더 고려하며,Each of the plurality of collected medical image data includes body shape information of the patient, and the body shape information is further considered when generating the dataset,상기 체형정보는, 상기 배액량에 영향을 주는 연령, 키 및 몸무게 중 적어도 하나에 대한 정보를 포함하는 것을 특징으로 하는, Characterized in that the body type information includes information on at least one of age, height, and weight that affects the amount of drainage.뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 제1항에 있어서,According to claim 1,상기 압력조절밸브에 상기 산출된 압력값이 적용된 이후, 상기 압력조절밸브에 부착된 센서로부터 실제 배액량에 대한 데이터가 수신되는 단계;receiving data on an actual displacement amount from a sensor attached to the pressure control valve after the calculated pressure value is applied to the pressure control valve;미리 설정된 주기 마다 미리 설정된 임계범위를 기반으로 상기 실제 배액량에 대한 이상 여부를 판단하는 단계; 및Determining whether or not the actual drainage amount is abnormal based on a preset threshold range at each preset period; and상기 판단 결과, 이상이 있는 것으로 판단되면, 알림정보를 생성하여 미리 등록된 적어도 하나의 관리자 단말로 송신하는 단계를 더 포함하는 것을 특징으로 하는,Characterized in that, as a result of the determination, if it is determined that there is an abnormality, generating notification information and transmitting it to at least one manager terminal registered in advance.뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 제6항에 있어서,According to claim 6,상기 알림정보는, 이상을 알리는 메시지 정보, 상기 실제 배양액을 측정한 측정값 정보 및 상기 압력을 낮추거나 높이기 위해 조절해야 하는 조절값 중 적어도 하나를 포함하고,The notification information includes at least one of message information informing of an abnormality, measurement value information obtained by measuring the actual culture medium, and an adjustment value to be adjusted to lower or increase the pressure,상기 실제 배액량에 대한 데이터는, 사물인터넷(Internet of Things, IoT)을 기반으로 수신되는 것을 특징으로 하는,Characterized in that the data on the actual drainage amount is received based on the Internet of Things (IoT),뇌실-복강 단락술을 위한 압력 산출 방법.Pressure calculation method for ventricular-abdominal shunting.
- 하드웨어인 컴퓨터와 결합되어, 제1항의 뇌실-복강 단락술을 위한 압력 산출 방법을 실행시키기 위하여 컴퓨터 판독가능 기록매체에 저장된 컴퓨터 프로그램.A computer program stored in a computer readable recording medium in order to execute the pressure calculation method for the ventricle-peritoneal shunting of claim 1 in combination with a computer that is hardware.
- 뇌실-복강 단락술을 위한 압력 산출 장치에 있어서,In the pressure calculating device for ventricular-peritoneal shunting,적어도 하나의 정보 또는 데이터를 송수신하는 통신모듈;A communication module for transmitting and receiving at least one piece of information or data;데이터셋을 이용하여 인공지능 모델을 학습하는 학습모듈;A learning module for learning an artificial intelligence model using a dataset;각종 정보 또는 데이터를 저장하고, 환자의 의료영상데이터에 대응하는 압력값을 산출하기 위해 필요한 적어도 하나의 프로세스를 저장하는 저장모듈; 및a storage module for storing various types of information or data and storing at least one process required to calculate a pressure value corresponding to the patient's medical image data; and상기 적어도 하나의 프로세스를 기반으로, 상기 환자의 의료영상데이터에 대응하는 압력값을 산출하기 위한 동작을 수행하는 제어모듈을 포함하며,A control module performing an operation for calculating a pressure value corresponding to the medical image data of the patient based on the at least one process,상기 제어모듈은,The control module,복수개의 의료영상데이터를 수집하고, 상기 수집된 복수개의 의료영상데이터 각각에 라벨링된 압력값을 확인하여 데이터셋을 생성하고, 상기 생성된 데이터셋을 이용하여 인공지능 모델에 대한 학습을 수행한 후, 의료영상데이터가 입력되면, 상기 학습된 인공지능 모델을 이용하여 상기 입력된 의료영상데이터에 대응하는 압력값을 산출하며,A plurality of medical image data is collected, a data set is created by checking the pressure value labeled on each of the collected plurality of medical image data, and an artificial intelligence model is learned using the generated data set. , When medical image data is input, a pressure value corresponding to the input medical image data is calculated using the learned artificial intelligence model,상기 압력값은, 뇌실에 삽입된 제1 카테터에서 복강에 삽입된 제2 카테터로 배액되는 뇌척수액의 배액량을 결정하는 것으로, 상기 제1 카테터 및 상기 제2 카테터 사이에 구비된 압력조절밸브에 적용되는 것을 특징으로 하는,The pressure value determines the drainage amount of cerebrospinal fluid drained from the first catheter inserted into the ventricle to the second catheter inserted into the abdominal cavity, and is applied to a pressure control valve provided between the first catheter and the second catheter characterized in that,뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
- 제9항에 있어서,According to claim 9,상기 복수개의 의료영상데이터는,The plurality of medical image data,뇌실-복강 단락술이 실시되어 뇌척수액을 배출하는 환자의 엑스레이 촬영 이미지이며,It is an X-ray image of a patient who has undergone ventricular-abdominal shunting and discharged cerebrospinal fluid,미리 설정된 기준에 따라 정상 동작으로 판단된 의료영상데이터들을 포함하는 것을 특징으로 하는,Characterized in that it includes medical image data determined to be normal operation according to preset criteria,뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
- 제9항에 있어서,According to claim 9,상기 제어모듈은, 상기 데이터셋을 생성할 시, 상기 수집된 복수개의 의료영상데이터 각각에 대해 압력조절밸브를 검출하고, 상기 검출된 압력조절밸브의 압력값들을 확인한 후, 상기 확인된 압력값을 해당 의료영상데이터에 라벨링하는 것을 특징으로 하는,When generating the dataset, the control module detects a pressure control valve for each of the plurality of collected medical image data, checks the pressure values of the detected pressure control valve, and then returns the checked pressure value. Characterized in that labeling the corresponding medical image data,뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
- 제9항에 있어서,According to claim 9,상기 검출된 압력조절밸브의 압력값은, 상기 검출된 압력조절밸브의 압력 게이지 인디케이터를 기반으로 확인하는 것을 특징으로 하는,Characterized in that the pressure value of the detected pressure control valve is confirmed based on the pressure gauge indicator of the detected pressure control valve.뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
- 제9항에 있어서,According to claim 9,상기 수집된 복수개의 의료영상데이터 각각은, 환자에 대한 체형정보를 포함하며,Each of the collected plurality of medical image data includes body shape information about the patient,상기 제어모듈은, 상기 데이터셋을 생성할 시, 상기 체형정보를 더 고려하며,The control module further considers the body shape information when generating the dataset,상기 체형정보는, 상기 배액량에 영향을 주는 연령, 키 및 몸무게 중 적어도 하나에 대한 정보를 포함하는 것을 특징으로 하는, Characterized in that the body type information includes information on at least one of age, height, and weight that affects the amount of drainage.뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
- 제9항에 있어서,According to claim 9,상기 제어모듈은, 상기 압력조절밸브에 상기 산출된 압력값이 적용된 이후, 상기 압력조절밸브에 부착된 센서로부터 실제 배액량에 대한 데이터가 수신되면, 미리 설정된 주기 마다 미리 설정된 임계범위를 기반으로 상기 실제 배액량에 대한 이상 여부를 판단하고, 상기 판단 결과, 이상이 있는 것으로 판단되면, 알림정보를 생성하여 미리 등록된 적어도 하나의 관리자 단말로 송신하는 것을 특징으로 하는,The control module, after the calculated pressure value is applied to the pressure control valve, when data on the actual displacement amount is received from a sensor attached to the pressure control valve, the actual displacement based on a preset threshold range for each preset period. Characterized in that determining whether there is an abnormality in the drainage amount and, as a result of the determination, if it is determined that there is an abnormality, notification information is generated and transmitted to at least one manager terminal registered in advance.뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
- 제14항에 있어서,According to claim 14,상기 알림정보는, 이상을 알리는 메시지 정보, 상기 실제 배양액을 측정한 측정값 정보 및 상기 압력을 낮추거나 높이기 위해 조절해야 하는 조절값 중 적어도 하나를 포함하고,The notification information includes at least one of message information informing of an abnormality, measurement value information obtained by measuring the actual culture medium, and an adjustment value to be adjusted to lower or increase the pressure,상기 실제 배액량에 대한 데이터는, 사물인터넷(Internet of Things, IoT)을 기반으로 수신되는 것을 특징으로 하는,Characterized in that the data on the actual drainage amount is received based on the Internet of Things (IoT),뇌실-복강 단락술을 위한 압력 산출 장치.Pressure Calculation Device for Ventricular-Abdominoplasty.
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KR20200067769A (en) * | 2018-12-04 | 2020-06-12 | 엘지전자 주식회사 | Medical image processing apparatus |
JP2021504816A (en) * | 2018-08-01 | 2021-02-15 | 中國醫藥大學附設醫院China Medical University Hospital | Bone age evaluation and height prediction model, its system and its prediction method |
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KR101754291B1 (en) * | 2017-04-04 | 2017-07-06 | 이현섭 | Medical image processing system and method for personalized brain disease diagnosis and status determination |
JP2021504816A (en) * | 2018-08-01 | 2021-02-15 | 中國醫藥大學附設醫院China Medical University Hospital | Bone age evaluation and height prediction model, its system and its prediction method |
KR20210084524A (en) * | 2018-10-26 | 2021-07-07 | 카를로스 에이. 하킴 | Self-Adjusting Hydrocephalus Valve |
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