KR20230007736A - Method to readout artificial knee joint loosening - Google Patents

Abstract

The present invention relates to a method for reading loosening state information of an artificial knee joint included in medical data. The method comprises the steps of: acquiring medical data including a knee joint image; preprocessing the acquired medical data; inputting the pre-processed medical data into a pre-trained artificial intelligence model to read a loosening state of an artificial knee joint; and generating result information about the medical data based on the read loosening state of the artificial knee joint. In the step of preprocessing of the acquired medical data, a target area for reading the loosening state of the artificial knee joint may be divided based on variations in shade in the acquired medical data. The present invention can provide treatment guide information.

Description

Artificial Knee Dissociation Reading Method {METHOD TO READOUT ARTIFICIAL KNEE JOINT LOOSENING}

The present invention relates to a method for reading dissociation of an artificial knee joint, and more particularly, to a method of reading dissociation state information of an artificial knee joint included in medical data.

In general, the development of modern medicine and the improvement of living standards have increased the average life expectancy, and in particular, the population of the elderly is increasing. In particular, in the 2000s, the number of elderly people living an active life beyond the age of 80 or 90 is increasing, and this trend is expected to increase over time. As such, human joints have been working for a longer period of time and must endure well for quality of life, but due to aging, patients suffering from naturally occurring degenerative arthritis are increasing, mainly in the elderly. In particular, when treating a knee joint damaged due to various causes including degenerative arthritis of the elderly, total knee arthroplasty, which removes the existing knee joint and inserts an artificial knee joint to replace the existing knee joint, is essential. The demand for total knee arthroplasty is steadily increasing due to the increase in the elderly population and the increase in average life expectancy, and the demand for reoperation to replace an artificial knee joint that has expired is also increasing. As the biggest cause of reoperation for total knee arthroplasty is the dissociation of the artificial knee joint, the diagnosis of dissociation is very important and its importance will increase even more.

In addition, there is a limit to the lifespan of the artificial knee joint, and reoperation of the patient is required due to dissociation of the artificial knee joint due to problems such as osteolysis in which bones are melted due to metal or polymer fragments caused by wear of the artificial knee joint. Diagnosis and judgment of such artificial knee joint dissociation is still a difficult problem, and in fact, it is often not confirmed until the operator accurately confirms it during surgery. Therefore, in addition to primary radiographic imaging, alternative imaging modalities such as computed tomography, bone scans and arthrography, and MRI can increase diagnostic accuracy, but are often invasive, insensitive or add cost, increase exposure to ionizing radiation, and There may be risks involved. This lack of validity or accuracy can lead to unnecessary hematological tests, additional imaging tests, and even unnecessary surgery.

Therefore, in the future, it is required to develop an artificial knee joint dissociation reading method using an artificial intelligence model to assist doctors in diagnosing artificial knee joint dissociation.

Republic of Korea Patent No. 10-2097741 (2020. 03. 31)

One object of the present invention to solve the above-described problems is to segment a target region from medical data including a knee joint image using an artificial intelligence model and read the dissociation state of the artificial knee joint, thereby determining the accuracy of the artificial knee joint dissociation determination. The purpose of this study is to provide an artificial knee joint dissociation reading method that can increase validity and provide treatment guide information.

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.

An artificial knee joint dissociation reading method according to an embodiment of the present invention for solving the above problems includes acquiring medical data including a knee joint image, preprocessing the obtained medical data, and preprocessing the medical data. The method may include reading an artificial knee joint dissociation state by inputting the input to a pre-learned artificial intelligence model, and generating result information for the medical data based on the read artificial knee joint dissociation state.

In an embodiment, the medical data including the knee joint image may include a knee joint image before surgery, a knee joint image after surgery, a knee joint image before reoperation, and a knee joint image after reoperation.

In an embodiment, the knee joint image before surgery and the knee joint image after surgery are medical data for a patient who underwent total knee arthroplasty, and the knee joint image before reoperation and knee joint image after reoperation are medical data for a patient who underwent total knee arthroplasty. It is characterized in that it is medical data for a patient.

In an embodiment, the medical data is characterized in that it includes a doctor's experience report on artificial knee joint dissociation, a surgical record sheet of a patient who underwent revision knee arthroplasty due to dissociation, and a surgical photograph.

In an embodiment, the step of pre-processing the acquired medical data may include segmenting a target region for reading artificial knee joint dislocation from the acquired medical data.

In an embodiment, the step of preprocessing the obtained medical data may include dividing a target region for reading the artificial knee joint dislocation based on a change in shading of the medical data.

In an embodiment, the step of pre-processing the obtained medical data is characterized in that it includes a junction between the implant and the bone.

In an embodiment, the pre-processing of the obtained medical data may include: first dividing a target region for artificial knee joint dissociation reading from the acquired medical data; and artificial knee joint dissociation reading from the first divided target region. It is characterized in that it includes the step of secondarily dividing the target region for

In an embodiment, the step of first dividing the target region includes first dividing the target region based on the shading change of the medical data, and second dividing the target region comprises: It is characterized in that the area around the joint point between the implant and the bone among the areas is secondarily divided into a plurality of target areas.

In an embodiment, the step of reading the dissociation state of the artificial knee joint is characterized by reading whether or not the artificial knee joint is dissociated through the artificial intelligence model.

In an embodiment, the step of reading the dissociation state of the artificial knee joint may include reading whether or not the artificial knee joint is dissociated through the artificial intelligence model, and as a result of the reading, if the dissociation progresses of the artificial knee joint, the cause of dissociation of the artificial knee joint It is characterized by reading.

In an embodiment, reading the cause of dissociation of the artificial knee joint may include reading at least one of a first cause of aging of the artificial knee joint and a second cause of inflammation of the artificial knee joint.

In an embodiment, the artificial intelligence model is characterized in that it is pre-learned to read dissociation of the artificial knee joint when a target region for reading dissociation of the artificial knee joint segmented from the medical data is input.

In an embodiment, the artificial intelligence model reads whether or not dissociation of the artificial knee joint is input when a target region for reading dissociation of the artificial knee joint divided from the medical data is input, and if the dissociation progress of the artificial knee joint as a result of the reading, the dissociation of the artificial knee joint is progressing. Characterized in that it is pre-learned to read the cause of dissociation of the artificial knee joint.

In an embodiment, the generating of result information on the medical data may include generating result information including a treatment guide based on the read dissociation state of the artificial knee joint.

In an embodiment, the step of generating result information for the medical data includes classifying a dissociation class based on the read dissociation state of the artificial knee joint, and including a treatment guide corresponding to the classified class. It is characterized by generating.

A computer program stored on a computer readable storage medium according to an embodiment of the present invention, when executed in one or more processors, performs the following operations for reading an artificial knee joint dissociation, the operations comprising: a knee joint image Obtaining medical data including, pre-processing the acquired medical data, inputting the pre-processed medical data to a pre-learned artificial intelligence model to read the dissociation state of the artificial knee joint, and the read artificial knee joint An operation of generating result information on the medical data based on the dissociation state may be included.

A computing device for providing a method for reading artificial knee joint dissociation according to an embodiment of the present invention, comprising a processor including one or more cores, and a memory, wherein the processor obtains medical data including a knee joint image, The acquired medical data is pre-processed, the pre-processed medical data is input to a pre-learned artificial intelligence model to read the dissociation state of the artificial knee joint, and result information on the medical data based on the read dissociation state of the artificial knee joint. can create

A user terminal for artificial knee joint dissociation reading according to an embodiment of the present invention, comprising a processor including one or more cores, a memory, and an output unit providing a user interface, wherein the user interface includes a response to a medical data input. , display result information on the medical data, and obtain medical data including a knee joint image, pre-process the obtained medical data, and pre-process the pre-processed medical data. The artificial knee joint dissociation state may be read by inputting into the learned artificial intelligence model, and may be generated based on the read artificial knee joint dissociation state.

In an embodiment, the user interface displays result information on the medical data in response to a user input, and the result information on the medical data obtains medical data including a knee joint image, The obtained medical data may be pre-processed, the pre-processed medical data may be input into a pre-learned artificial intelligence model to read the dissociation state of the artificial knee joint, and may be generated based on the read dissociation state of the artificial knee joint.

A computer program providing a method for reading artificial knee joint dissociation according to another embodiment of the present invention for solving the above problems is combined with a computer that is hardware and stored in a medium to perform any one of the above methods.

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a computer program for executing the method may be further provided.

As described above, according to the present invention, by segmenting the target region from medical data including knee joint images using an artificial intelligence model and reading the dissociation state of the artificial knee joint, the accuracy and validity of the artificial knee joint dissociation judgment is improved, and the treatment guide information can provide.

In addition, the present invention can reduce additional examination costs and unnecessary surgery through accurate determination of artificial knee joint dissociation after total knee arthroplasty, thereby helping to reduce medical costs not only for patients but also for the country, and life to patients. The optimal treatment plan for quality improvement can be presented quickly and promptly.

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.

1 is a block diagram of a computing device that performs an operation for providing a method for reading artificial knee joint dissociation according to an embodiment of the present invention.
2 is a schematic diagram showing a network function of an artificial intelligence model according to an embodiment of the present invention.
3 is a diagram for explaining a preprocessing process according to an embodiment of the present invention.
4 is a flowchart illustrating a method for reading artificial knee joint dissociation according to an embodiment of the present invention.

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.

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.

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.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Prior to the description, the meaning of the terms used in this specification will be briefly described. However, it should be noted that the description of terms is intended to help the understanding of the present specification, and is not used in the sense of limiting the technical spirit of the present invention unless explicitly described as limiting the present invention.

In this specification, neural networks, artificial neural networks, and network functions may often be used interchangeably.

The term "image" or "image data" as used throughout the description and claims of the present invention refers to multidimensional data composed of discrete image elements (e.g., pixels in the case of a two-dimensional image); in other words, ( A term used to refer to a visible object (e.g., displayed on a video screen) or a digital representation of that object (e.g., a file corresponding to the pixel output of a CT, MRI detector, etc.).

For example, "image" or "image" may refer to computed tomography (CT), magnetic resonance imaging (MRI), knee joint imaging, ultrasound, or any other medical imaging known in the art. It may be a medical image of a subject collected by the system. The image does not necessarily have to be provided in a medical context, but may be provided in a non-medical context, such as X-ray imaging for security screening.

Throughout the detailed description and claims of the present invention, the 'DICOM (Digital Imaging and Communications in Medicine)' standard is a term that collectively refers to various standards used for digital image expression and communication in medical devices, The DICOM standard is published by a joint committee formed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA).

In addition, throughout the detailed description and claims of the present invention, 'Picture Archiving and Communication System (PACS)' is a term that refers to a system that stores, processes, and transmits according to the DICOM standard, and includes X-ray, CT, , Medical imaging images acquired using digital medical imaging equipment such as MRI are stored in DICOM format and can be transmitted to terminals inside and outside the hospital through a network, and reading results and medical records can be added to them.

Also, throughout this specification, a neural network, a neural network, and a network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

1 is a block diagram of a computing device that performs an operation for providing a method for reading artificial knee joint dissociation according to an embodiment of the present invention.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present invention, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

In the present invention, the processor 110 relates to a method for generating result information by reading artificial knee joint dissociation included in medical data, obtaining medical data including a knee joint image, pre-processing the obtained medical data, The artificial knee joint dissociation state may be read by inputting the preprocessed medical data to the pre-learned artificial intelligence model, and result information on the medical data may be generated based on the read artificial knee joint dissociation state.

Here, the medical data may include at least one of image data, audio data, and time series data. That is, any type of data by which a person engaged in the medical industry or a device for diagnosis can determine whether or not a disease exists in the data may be included in the medical data. The image data includes all image data that is output after photographing or measuring a patient's affected part through examination equipment and turning it into an electrical signal. The image data may include image data constituting each frame of a video in a video continuously photographed over time from a medical imaging device. For example, it includes ultrasound examination image data, image data by an MRI device, CT tomography image data, X-ray image data, and the like. Furthermore, when audio data is converted into an electrical signal and output as an image in the form of a graph or time-series data is expressed as visualized data such as a graph, the image or data may be included in the video data. For example, medical data may include CT images. The foregoing example of medical data is only an example and does not limit the present disclosure.

As an embodiment, the medical data including the knee joint image may include a knee joint image before surgery, a knee joint image after surgery, a knee joint image before revision surgery, and a knee joint image after revision surgery.

Here, the knee joint image before surgery and the knee joint image after surgery may be medical data for a patient who has undergone total knee arthroplasty, and the knee joint image before and after revision surgery may be medical data for a patient who has undergone total knee arthroplasty. It could be medical data.

That is, the knee joint image before revision surgery and the knee joint image after revision surgery may be medical data for a patient who has undergone revision knee arthroplasty due to dissociation.

In addition, the medical data may include a doctor's experience report on artificial knee joint dissociation, a surgical record sheet and surgery photos of a patient who underwent revision knee arthroplasty due to dissociation.

Next, when pre-processing the acquired medical data, the processor 110 may segment an object region for reading artificial knee joint dissociation from the acquired medical data.

Here, the target region may define a range in which dissociation is to be read from a medical image, but is not limited thereto.

For example, when preprocessing the obtained medical data, the processor 110 may divide a target region for reading artificial knee joint dissociation based on a shade change of the medical data.

That is, the processor 110, when preprocessing the acquired medical data, calculates a shadow change rate from the medical data, detects a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for artificial knee joint dissociation reading, and detects the target area can be divided.

For example, the target region for artificial knee joint dissociation reading may include a junction point between an implant and a bone.

As another case, in the present invention, when pre-processing the acquired medical data, the processor 110 firstly divides a target region for artificial knee joint dissociation reading from the obtained medical data, and artificially divides the target region from the primary divided target region. A target region for knee dissociation reading may be secondarily divided.

For example, the processor 110 may first divide the target region based on a shading change of medical data when first dividing the target region.

That is, the processor 110, when first dividing the target region, calculates a shadow change rate from medical data, detects a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for artificial knee joint dissociation reading, and detects the target region. The area can be first divided.

In addition, when the target region is secondarily divided, the processor 110 may secondarily divide the region around the junction between the implant and the bone among the firstly divided target regions into a plurality of target regions.

Here, as for the plurality of target regions, an area around a junction point between an implant and a bone may be divided into seven target regions, which is only an example, but is not limited thereto.

For example, the plurality of target regions include a first target region for the lower region of the right edge of the tibial side implant, a second target region for the lower region between the right edge of the tibial side implant and the insertion rib, among the regions around the junction between the implant and the bone. 2 target area, 3rd target area for the lower area between the left edge of the tibial side implant and the insertion rib, 4th target area for the lower area of the left edge of the tibial side implant, 5th target for the area around the right side of the insertion rib region, a sixth target region for the lower region of the insertion rib, and a seventh target region for the area around the left side of the insertion rib, which is merely an embodiment, but is not limited thereto.

Next, when reading the artificial knee joint dissociation state, the processor 110 may read whether the artificial knee joint dissociates or not through the artificial intelligence model.

In some cases, when reading the artificial knee joint dissociation state, the processor 110 reads whether or not the artificial knee joint has dissociated through an artificial intelligence model, and reads the cause of dissociation of the artificial knee joint if the dissociation progresses of the artificial knee joint as a result of the reading. You may.

For example, when reading the cause of dissociation of the artificial knee joint, the processor 110 may read at least one of a first cause of aging of the artificial knee joint and a second cause of inflammation of the artificial knee joint.

Next, the artificial intelligence model of the present invention may include a machine learning model including a neural network.

That is, the artificial intelligence model of the present invention may be pre-trained to read dissociation of the artificial knee joint when a target region for reading dissociation of the artificial knee joint divided from medical data and a target region are input.

In some cases, the artificial intelligence model of the present invention reads the dissociation of the artificial knee joint when the target region for reading artificial knee joint dissociation divided from medical data and the target region are input, and as a result of the reading, if dissociation of the artificial knee joint progresses, artificial intelligence model It can be pretrained to read the causes of dissociation of the knee joint.

Also, in the present invention, when generating result information on medical data, the processor 110 may generate result information including a treatment guide based on the read dissociation state of the artificial knee joint.

Here, the treatment guide may include reoperation time information, which is only an embodiment, but is not limited thereto.

In some cases, in the present invention, when generating result information on medical data, the processor 110 classifies a dissociation class based on the read dissociation state of the artificial knee joint, and provides a treatment guide corresponding to the classified class. It is also possible to generate result information including.

Here, the class may indicate a class related to an artificial knee joint dissociation state.

For example, the class may include at least one of a low-risk group, a medium-risk group, and a high-risk group corresponding to normal, abnormal, mild, severe, and treatment prognoses, which are examples, but are not limited thereto.

And, the treatment guide may include reoperation time information corresponding to the classified class, which is only an embodiment, but is not limited thereto.

Also, the aforementioned artificial intelligence model may be a deep neural network. Throughout this specification, neural network, network function, and neural network may be used interchangeably. A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), and a deep belief network (DBN). , Q network, U network, Siamese network, and the like.

A convolutional neural network is a type of deep neural network and includes a neural network including a convolutional layer. A convolutional neural network is a type of multilayer perceptron designed to use minimal preprocessing. A CNN may be composed of one or several convolutional layers and artificial neural network layers combined therewith. CNNs can additionally utilize weights and pooling layers. Thanks to this structure, CNNs can fully utilize the two-dimensional structure of the input data. Convolutional neural networks can be used to recognize objects in images. A convolutional neural network can represent and process image data as a matrix having dimensions. For example, in the case of image data encoded in RGB (red-green-blue), each R, G, and B color may be represented as a two-dimensional (eg, two-dimensional image) matrix. That is, the color value of each pixel of the image data can be a component of a matrix, and the size of the matrix can be the same as the size of the image. Thus, image data can be represented by three two-dimensional matrices (a three-dimensional data array).

In a convolutional neural network, a convolutional process (input/output of a convolutional layer) can be performed by moving the convolutional filter and multiplying the convolutional filter with matrix components at each position of the image. A convolutional filter may be composed of an n*n matrix. A convolutional filter can consist of a fixed shape filter, which is usually smaller than the total number of pixels in an image. That is, when an m*m image is input to a convolutional layer (for example, a convolutional layer whose size of convolutional filter is n*n), a matrix representing n*n pixels including each pixel of the image It can be component multiplied with this convolutional filter (ie, multiplication of each component of the matrix). A component matching the convolutional filter can be extracted from the image by multiplication with the convolutional filter. For example, a 3*3 convolutional filter to extract top and bottom linear components from an image would be constructed as [[0,1,0], [0,1,0], [0,1,0]] can When a 3*3 convolutional filter for extracting up and down linear components from an image is applied to an input image, up and down linear components matching the convolutional filter may be extracted from the image and output. The convolutional layer may apply a convolutional filter to each matrix for each channel representing an image (ie, R, G, B colors in the case of an R, G, B coded image). The convolutional layer may extract features matching the convolutional filter from the input image by applying the convolutional filter to the input image. The filter value of the convolutional filter (that is, the value of each element of the matrix) may be updated by backpropagation during the learning process of the convolutional neural network.

A subsampling layer is connected to the output of the convolutional layer to simplify the output of the convolutional layer, thereby reducing memory usage and computational complexity. For example, if the output of the convolutional layer is input to a pooling layer with a 2*2 max pooling filter, the image is compressed by outputting the maximum value included in each patch for each 2*2 patch in each pixel of the image. can The aforementioned pooling may be a method of outputting a minimum value of a patch or an average value of a patch, and any pooling method may be included in the present invention.

A convolutional neural network may include one or more convolutional layers and subsampling layers. A convolutional neural network may extract features from an image by repeatedly performing a convolutional process and a subsampling process (eg, max pooling described above). Through an iterative convolutional process and subsampling process, a neural network can extract global features of an image.

An output of the convolutional layer or the subsampling layer may be input to a fully connected layer. A fully connected layer is a layer in which all neurons in one layer are connected to all neurons in neighboring layers. A fully connected layer may refer to a structure in which all nodes of each layer are connected to all nodes of other layers in a neural network.

According to an embodiment of the present invention, the processor 110 may be composed of one or more cores, a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU) ), a processor for data analysis and deep learning, such as a tensor processing unit (TPU). The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present invention. According to an embodiment of the present invention, the processor 110 may perform an operation for learning a neural network. The processor 110 performs neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed for At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in one embodiment of the present invention, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present invention may be a CPU, GPGPU or TPU executable program.

According to one embodiment of the present invention, the memory 130 may store a computer program for performing an artificial knee joint dissociation reading and providing a dissociation reading result, and the stored computer program may be read and driven by the processor 120. there is. The memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present invention, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Memory) Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above memory is only an example, and is not limited thereto.

The network unit 150 according to an embodiment of the present invention may transmit and receive artificial knee joint dissociation read result information and the like to other computing devices and servers. In addition, the network unit 150 may enable communication between a plurality of computing devices so that operations for artificial knee joint dissociation reading or model learning may be performed in a distributed manner in each of the plurality of computing devices. The network unit 150 may enable communication between a plurality of computing devices to perform distributed processing of calculations for artificial knee joint dissociation reading or model learning using a network function.

The network unit 150 according to an embodiment of the present invention may operate based on any type of wired or wireless communication technology currently used and implemented, such as short-distance (short-distance), long-distance, wired, and wireless, and other networks. can also be used in

The computing device 100 of the present invention may further include an output unit and an input unit.

The output unit according to an embodiment of the present invention may display a user interface (UI) for providing an artificial knee joint dissociation reading result. The output unit may output any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

In one embodiment of the present invention, the output unit is a liquid crystal display (liquid crystal display, LCD), thin film transistor liquid crystal display (thin film transistor-liquid crystal display, TFT LCD), organic light-emitting diode (organic light-emitting diode, OLED) , a flexible display, and a 3D display. Some of these display modules may be of a transparent type or a light transmissive type so that the outside can be seen through them. This may be referred to as a transparent display module, and a representative example of the transparent display module is TOLED (Transparent OLED) and the like.

The input unit according to an embodiment of the present invention may receive a user input. The input unit may include keys and/or buttons on a user interface for receiving user input, or physical keys and/or buttons. A computer program for controlling a display according to embodiments of the present invention may be executed according to a user input through an input unit.

The input unit according to embodiments of the present invention may detect a user's button operation or touch input and receive a signal, or may receive a user's voice or motion through a camera or microphone and convert it into an input signal. For this purpose, speech recognition technology or motion recognition technology may be used.

The input unit according to embodiments of the present invention may be implemented as an external input device connected to the computing device 100 . For example, the input device may be at least one of a touch pad, a touch pen, a keyboard, or a mouse for receiving a user input, but this is only an example and is not limited thereto.

The input unit according to an embodiment of the present invention may recognize a user touch input. The input unit according to an embodiment of the present invention may have the same configuration as the output unit. The input unit may include a touch screen implemented to receive a user's selection input. The touch screen may use any one of a contact capacitive method, an infrared light sensing method, a surface ultrasonic (SAW) method, a piezoelectric method, and a resistive film method. Detailed description of the touch screen described above is only an example according to an embodiment of the present invention, and various touch screen panels may be employed in the computing device 100 . The input unit configured as a touch screen may include a touch sensor. The touch sensor may be configured to convert a change in pressure applied to a specific portion of the input unit or capacitance generated at a specific portion of the input unit into an electrical input signal. The touch sensor may be configured to detect not only the touched position and area, but also the pressure upon touch. When there is a touch input to the touch sensor, the corresponding signal(s) is sent to the touch controller. The touch controller may process the signal(s) and then transmit corresponding data to processor 110 . Accordingly, the processor 110 can recognize which area of the input unit has been touched.

In one embodiment of the present invention, the server may include other configurations for performing the server environment of the server. The server may include any type of device. The server may be a digital device, such as a laptop computer, a notebook computer, a desktop computer, a web pad, or a mobile phone, equipped with a processor and having an arithmetic capability with a memory.

A server (not shown) performing an operation for providing a user interface displaying a result of reading artificial knee joint dissociation according to an embodiment of the present invention to a user terminal may include a network unit, a processor, and a memory.

The server may generate a user interface according to embodiments of the present invention. The server may be a computing system that provides information to clients (eg, user terminals) over a network. The server may transmit the generated user interface to the user terminal. In this case, the user terminal may be any type of computing device 100 capable of accessing the server. The processor of the server may transmit the user interface to the user terminal through the network unit. A server according to embodiments of the present invention may be, for example, a cloud server. The server may be a web server that processes services. The types of servers described above are examples only and are not limited thereto.

Therefore, the present invention increases the accuracy and validity of artificial knee joint dissociation determination and provides treatment guide information by segmenting a target region from medical data including knee joint images using an artificial intelligence model and reading the artificial knee joint dissociation state. can do.

In addition, the present invention can reduce additional examination costs and unnecessary surgery through accurate determination of artificial knee joint dissociation after total knee arthroplasty, thereby helping to reduce medical costs not only for patients but also for the country, and life to patients. The optimal treatment plan for quality improvement can be presented quickly and promptly.

2 is a schematic diagram showing a network function of an artificial intelligence model according to an embodiment of the present invention.

Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present invention, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present invention may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer, and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present invention is a type of neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the number of nodes progresses from the input layer to the hidden layer. can A neural network according to another embodiment of the present invention may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted boltzmann machines (RBMs). machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example and is not limited thereto.

In one embodiment of the present invention, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to dimensions after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate may be used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly obtain a certain level of performance, and a low learning rate may be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer should be applied. can

3 is a diagram for explaining a preprocessing process according to an embodiment of the present invention.

As shown in FIG. 3 , in the present invention, when medical data including a knee joint image is obtained, the acquired medical data may be pre-processed.

Here, in the present invention, when pre-processing the acquired medical data, an object region for artificial knee joint dissociation reading may be segmented from the acquired medical data.

For example, in the present invention, when pre-processing acquired medical data, a target region for artificial knee joint dissociation reading may be divided based on a shading change of the medical data.

That is, the present invention, when pre-processing the acquired medical data, calculates a shadow change rate from the medical data, detects a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading artificial knee joint dissociation, and determines the detected target region can be divided

For example, the target region for artificial knee joint dissociation reading may include a junction point between an implant and a bone.

In some cases, the present invention, when pre-processing the acquired medical data, firstly divides a target region for artificial knee joint dissociation reading from the acquired medical data, and obtains a target for artificial knee joint dissociation reading from the primary divided target region. It is also possible to divide the area into a second division.

For example, when the target region is firstly divided, the target region may be firstly divided based on a shading change of medical data.

That is, in the present invention, when the target region is first segmented, a shadow change rate is calculated from medical data, a region in which the calculated shadow change rate is equal to or greater than a reference value is detected as a target region for artificial knee joint dissociation reading, and the detected target region is Can be divided into 1st.

In addition, when the target region is secondarily divided, the region around the junction between the implant and the bone among the firstly divided target regions may be secondarily divided into a plurality of target regions.

Here, as for the plurality of target regions, an area around a junction point between an implant and a bone may be divided into seven target regions, which is only an example, but is not limited thereto.

For example, as shown in FIG. 3 , a plurality of target regions include a first target region for the lower right edge area of the tibial side implant among the areas around the junction point between the implant and the bone, between the right edge of the tibial side implant and the insertion rib. A second target area for the lower area, a third target area for the lower area between the left edge of the tibial side implant and the insertion rib, a fourth target area for the lower area of the left edge of the tibial side implant, an area around the right side of the insertion rib It may include a fifth target region for the insertion rib, a sixth target region for the bottom region of the insertion rib, and a seventh target region for the area around the left side of the insertion rib, which are examples only, but are not limited thereto.

In the present invention, it is possible to read the dissociation of a target region pre-processed and subdivided into a plurality of segments through a pre-learned artificial intelligence model.

In some cases, the present invention, when reading the dissociation state of the artificial knee joint, reads whether the artificial knee joint is dissociated through an artificial intelligence model, and as a result of the reading, if the dissociation progresses of the artificial knee joint, the cause of dissociation of the artificial knee joint may be read. there is.

For example, when reading the cause of dissociation of the artificial knee joint, the present invention may read at least one of a first cause of aging of the artificial knee joint and a second cause of inflammation of the artificial knee joint.

4 is a flowchart illustrating a method for reading artificial knee joint dissociation according to an embodiment of the present invention.

As shown in FIG. 4 , according to the present invention, medical data including a knee joint image may be acquired (S10).

As an embodiment, the medical data including the knee joint image may include a knee joint image before surgery, a knee joint image after surgery, a knee joint image before revision surgery, and a knee joint image after revision surgery.

Here, the knee joint image before surgery and the knee joint image after surgery may be medical data for a patient who has undergone total knee arthroplasty, and the knee joint image before and after revision surgery may be medical data for a patient who has undergone total knee arthroplasty. It could be medical data.

That is, the knee joint image before revision surgery and the knee joint image after revision surgery may be medical data for a patient who has undergone revision knee arthroplasty due to dissociation.

In addition, the medical data may include a doctor's experience report on artificial knee joint dissociation, a surgical record sheet and surgery photos of a patient who underwent revision knee arthroplasty due to dissociation.

Next, the present invention may pre-process the acquired medical data (S20).

Here, the present invention may segment an object region for artificial knee joint dissociation reading from the obtained medical data.

For example, the present invention may divide a target region for artificial knee joint dissociation reading based on a shade change of medical data.

For example, the target region for artificial knee joint dissociation reading may include a junction point between an implant and a bone.

In another case, the present invention may firstly divide a target region for reading artificial knee joint dissociation from acquired medical data, and secondly divide a target region for reading artificial knee joint dissociation from the firstly divided target region.

For example, according to the present invention, when the target region is firstly divided, the target region is firstly divided based on the shading change of medical data, and when the target region is secondly divided, implants and implants among the firstly divided target regions A region around the joint point of bones may be secondarily divided into a plurality of target regions.

Here, as for the plurality of target regions, an area around a junction point between an implant and a bone may be divided into seven target regions, which is only an example, but is not limited thereto.

Subsequently, the present invention may read the dissociation state of the artificial knee joint by inputting the preprocessed medical data to the pre-learned artificial intelligence model (S30).

In some cases, the present invention, when reading the dissociation state of the artificial knee joint, reads whether the artificial knee joint is dissociated through an artificial intelligence model, and as a result of the reading, if the dissociation progresses of the artificial knee joint, the cause of dissociation of the artificial knee joint may be read. there is.

For example, when reading the cause of dissociation of the artificial knee joint, the present invention may read at least one of a first cause of aging of the artificial knee joint and a second cause of inflammation of the artificial knee joint.

In addition, the present invention may generate result information on medical data based on the read dissociation state of the artificial knee joint (S40).

For example, the present invention may generate result information including a treatment guide based on the read dissociation state of the artificial knee joint.

Here, the treatment guide may include reoperation time information, which is only an embodiment, but is not limited thereto.

In some cases, the present invention may classify a dissociation class based on the read dissociation state of the artificial knee joint, and generate result information including a treatment guide corresponding to the classified class.

Here, the class may indicate a class related to an artificial knee joint dissociation state.

For example, the class may include at least one of a low-risk group, a medium-risk group, and a high-risk group corresponding to normal, abnormal, mild, severe, and treatment prognoses, which are examples, but are not limited thereto.

And, the treatment guide may include reoperation time information corresponding to the classified class, which is only an embodiment, but is not limited thereto.

As such, the present invention increases the accuracy and validity of artificial knee joint dissociation determination by segmenting a target region from medical data including knee joint images and reading the artificial knee joint dissociation state using an artificial intelligence model, and providing treatment guide information. can provide

In addition, the present invention can reduce additional examination costs and unnecessary surgery through accurate determination of artificial knee joint dissociation after total knee arthroplasty, thereby helping to reduce medical costs not only for patients but also for the country, and life to patients. The optimal treatment plan for quality improvement can be presented quickly and promptly.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The aforementioned program is C, C++, JAVA, machine language, etc. It may include a code coded in a computer language of. These codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related codes for which location (address address) of the computer's internal or external memory should be referenced for additional information or media required for the computer's processor to execute the functions. there is. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected through a network, and computer readable codes may be stored in a distributed manner.

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.

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 (20)

obtaining medical data including a knee joint image;
pre-processing the acquired medical data;
reading the dissociation state of the artificial knee joint by inputting the preprocessed medical data into a pre-learned artificial intelligence model; and
An artificial knee joint dissociation reading method comprising the step of generating result information about the medical data based on the read artificial knee joint dissociation state. According to claim 1,
Medical data including the knee joint image,
An artificial knee dissociation reading method comprising: a knee joint image before surgery, a knee joint image after surgery, a knee joint image before revision surgery, and a knee joint image after revision surgery. According to claim 2,
The knee joint image before the surgery and the knee joint image after the surgery,
Medical data on patients who underwent total knee arthroplasty,
The knee joint image before the reoperation and the knee joint image after the reoperation,
Artificial knee joint dissociation reading method, characterized in that the medical data for the patient who has undergone revision knee arthroplasty. According to claim 1,
The medical data,
An artificial knee joint dissociation reading method comprising a doctor's experience report on artificial knee joint dissociation, a surgical record sheet and surgical photos of a patient who underwent revision knee joint replacement surgery due to dissociation. According to claim 1,
The step of pre-processing the acquired medical data,
Artificial knee joint dissociation reading method, characterized in that for segmenting (segment) a target region for artificial knee joint dissociation reading from the obtained medical data. According to claim 5,
The step of pre-processing the acquired medical data,
Artificial knee joint dissociation reading method, characterized in that for dividing the target region for the artificial knee joint dissociation reading based on the shade change of the medical data. According to claim 1,
The step of pre-processing the acquired medical data,
firstly segmenting a target region for artificial knee joint dissociation reading from the obtained medical data; and
The artificial knee joint dissociation reading method comprising the step of secondarily dividing a target region for artificial knee joint dissociation reading from the primary divided target region. According to claim 7,
The step of first dividing the target region,
Artificial knee joint dissociation reading method, characterized in that for dividing based on the shade change of the medical data. According to claim 7,
The step of dividing the target region into a second step,
The artificial knee joint dissociation reading method, characterized in that the secondary division of the region around the junction point of the implant and the bone among the primary divided target regions into a plurality of target regions. According to claim 1,
The step of reading the artificial knee joint dissociation state,
Artificial knee joint dissociation reading method, characterized in that for reading whether the artificial knee joint is dissociated through the artificial intelligence model. According to claim 1,
The step of reading the artificial knee joint dissociation state,
The artificial knee joint dissociation reading method, characterized in that by reading the dissociation of the artificial knee joint through the artificial intelligence model, and reading the cause of dissociation of the artificial knee joint if the dissociation progress of the artificial knee joint as a result of the reading. According to claim 11,
Reading the cause of dissociation of the artificial knee joint,
The artificial knee joint dissociation reading method, characterized in that for reading at least any one of the first cause of aging of the artificial knee joint and the second cause of inflammation of the artificial knee joint. According to claim 1,
The step of generating result information for the medical data,
An artificial knee joint dissociation reading method characterized in that for generating result information including a treatment guide based on the read artificial knee joint dissociation state. According to claim 1,
The step of generating result information for the medical data,
Classification of dissociation based on the read dissociation state of the artificial knee joint, and generating result information including a treatment guide corresponding to the classified class. A computer program stored on a computer readable storage medium, which, when executed on one or more processors, causes the computer program to perform the following operations for reading artificial knee joint dislocation, the operations comprising:
obtaining medical data including a knee joint image;
pre-processing the acquired medical data;
reading the dissociation state of the artificial knee joint by inputting the preprocessed medical data into a pre-learned artificial intelligence model; and
A computer program stored in a computer readable storage medium comprising an operation of generating result information about the medical data based on the read dissociation state of the artificial knee joint. A computing device for providing an artificial knee joint dissociation reading method,
a processor comprising one or more cores; and
Memory;
including,
the processor,
Obtaining medical data including a knee joint image,
Preprocessing the acquired medical data,
The preprocessed medical data is input into a pre-learned artificial intelligence model to read the dissociation state of the artificial knee joint, and
Computing device characterized in that for generating result information on the medical data based on the read dissociation state of the artificial knee joint. As a user terminal for artificial knee joint dissociation reading,
a processor comprising one or more cores;
Memory; and
Includes an output unit providing a user interface;
The user interface,
In response to the medical data input, display result information for the medical data, and
The resulting information on the medical data,
Obtaining medical data including a knee joint image, pre-processing the obtained medical data, inputting the pre-processed medical data to a pre-learned artificial intelligence model to read the dissociation state of the artificial knee joint, and reading the dissociation state of the artificial knee joint A user terminal characterized in that generated based on. According to claim 17,
The resulting information on the medical data,
A user terminal comprising a treatment guide corresponding to the read artificial knee joint dissociation state. According to claim 17,
The resulting information on the medical data,
A user terminal comprising a class related to the read dissociation state of the artificial knee joint and a treatment guide corresponding to the class. According to claim 17,
The user interface,
In response to user input, display result information about the medical data, and
The resulting information on the medical data,
Obtaining medical data including a knee joint image, pre-processing the obtained medical data, inputting the pre-processed medical data to a pre-learned artificial intelligence model to read the dissociation state of the artificial knee joint, and reading the dissociation state of the artificial knee joint A user terminal characterized in that generated based on.

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