Medical information processing apparatus, medical information processing method, and program

Abstract

To select an appropriate medical application.SOLUTION: A medical information processing apparatus according to an embodiment includes an acquisition unit and a selection unit. The acquiring unit acquires examination information including categorized data related to an examination of a subject. The selecting unit selects a target medical application to be used for processing related to the examination from among a plurality of medical applications based on the categorized data.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to a medical information processing device, a medical information processing method, and a program. [Background technology]

[0002] In recent years, machine learning technologies using artificial intelligence (AI) have been used in a variety of systems, and machine learning technologies are also being widely applied in the medical industry. Medical applications such as analytical applications using machine learning are used, for example, in the examination of subjects, for example, to analyze examinations using medical images obtained by photographing subjects using medical imaging equipment such as X-ray CT scanners and MRI scanners.

[0003] A wide variety of medical apps have been developed and provided, and medical professionals can select from a wide range of apps to process test results. However, the wide variety of apps available makes it difficult to select an appropriate app. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2020-204970 Summary of the Invention [Problem to be solved by the invention]

[0005] The problem to be solved by the embodiments disclosed in this specification and the drawings is to enable selection of an appropriate medical app. However, the problem to be solved by the embodiments disclosed in this specification and the drawings is not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0006] According to an embodiment, the medical information processing apparatus includes an acquisition unit and a selection unit. The acquisition unit acquires test information including categorization data related to a test of a subject. The selection unit selects a target medical app to be used for processing related to the test from among a plurality of medical apps based on the categorization data. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of a medical information processing system 1 according to a first embodiment. [Figure 2] FIG. 1 is a block diagram showing an example of the configuration of a medical information processing apparatus 100 according to a first embodiment. [Figure 3] FIG. 10 is a diagram showing an example of the contents of a medical application group 151. [Figure 4] FIG. 2 is a diagram showing an example of the contents of a medical application DB 152. [Figure 5] 4 is a flowchart showing an example of processing by the medical information processing apparatus 100 of the first embodiment. [Figure 6] FIG. 2 is a diagram for explaining the flow of processing in the medical information processing apparatus 100 of the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of a comparison of categorized data of medical application information and examination information. [Figure 8] FIG. 10 is a block diagram showing an example of the configuration of a medical information processing apparatus 200 according to a second embodiment. [Figure 9] 10 is a flowchart showing an example of processing by the medical information processing apparatus 200 of the second embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of updating medical application information based on categorized data of examination information. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, a medical information processing device, a medical information processing method, and a program according to embodiments will be described with reference to the drawings. The medical information processing device according to the embodiments selects a medical app to be used in an in-hospital system installed in a hospital, for example. The medical information processing device according to the embodiments is, for example, a device that performs processing related to an examination using medical images captured by a modality, such as analysis of the examination. The medical information processing device according to the embodiments selects a medical app to be used in the analysis of the examination (hereinafter, referred to as a target medical app) from multiple medical apps. The medical information processing device is installed in, for example, a cloud. The medical information processing device may be installed in a hospital or may be part of the in-hospital system.

[0009] (First embodiment) 1 is a block diagram showing an example of the configuration of a medical information processing system 1. The medical information processing system 1 includes, for example, an in-hospital system 2 and a medical information processing device 100 installed in a hospital. The in-hospital system 2 and the medical information processing device 100 can communicate with each other via a network NW. The network NW includes a LAN (Local Area Network), the Internet, a cellular network, a Wi-Fi network, a WAN (Wide Area Network), etc.

[0010] The in-hospital system 2 includes, for example, a Hospital Information System (hereinafter, HIS) 10, a Radiology Information System (hereinafter, RIS) 20, a medical image diagnostic device (modality) 30, and a Picture Archiving and Communication System (PACS) 40. The HIS 10, the RIS 20, the modality 30, and the PACS 40 can communicate with each other via an in-hospital network such as a LAN.

[0011] The HIS 10 is a computer system that supports operations within a hospital. Specifically, the HIS 10 has various subsystems, such as an electronic medical record system, a medical accounting system, a medical appointment system, a hospital reception system, and an admission and discharge management system.

[0012] The HIS 10 includes a computer such as a server device or a client terminal that includes a processor such as a CPU (Central Processing Unit), memories such as a ROM (Read Only Memory) and a RAM (Random Access Memory), a display, an input interface, and a communication interface.

[0013] A user inputs and references patient information using an electronic medical record system included in the HIS 10. A user issues an order for an imaging examination to the HIS 10. The HIS 10 transfers order information corresponding to the imaging examination order to other systems such as the RIS 20.

[0014] The RIS 20 is a computer system that supports operations in the imaging diagnostic department. The RIS 20 manages reservations for imaging examination orders in cooperation with the HIS 10, as well as links reservation information to examination equipment and manages examination information. The RIS 20 includes, for example, a computer such as a server device or a client terminal that has a processor such as a CPU, memories such as ROM and RAM, a display, an input interface, and a communication interface.

[0015] In response to an operation by a user such as a doctor or technician, the RIS 20 can transmit a recommendation request for a target medical app suitable for an examination to the medical information processing device 100. When an examination is performed on a subject, the RIS 20 generates examination information including categorized data related to the examination of the subject.

[0016] The RIS 20 transmits examination information together with a recommendation request to the medical information processing device 100. The examination information includes categorization data related to the examination of the subject. The categorization data includes at least one of the following data: the manufacturer name of the modality 30, imaging information at the time of imaging the subject, subject information related to the subject, and examination findings.

[0017] The examination information may also include collaborative app information regarding the apps used when multiple medical apps are used to analyze an examination, or segmentation information when a segmentation app is used as a medical app. The imaging information includes, for example, imaging conditions, examination protocols, and order information. The imaging information may be a part of this information. The subject information includes, for example, the gender, age, race, height, or weight of the subject. The subject information may be a part of this information.

[0018] The modality 30 performs imaging (photography) in accordance with imaging conditions (imaging protocol) determined based on, for example, an imaging test instruction. Examples of the modality 30 include an X-ray computed tomography apparatus, an X-ray diagnostic apparatus, a magnetic resonance imaging apparatus, an ultrasound diagnostic apparatus, and a nuclear medicine diagnostic apparatus. The modality 30 is operated by an operator such as a doctor (radiologist) or a diagnostic radiologist. Medical images generated by imaging with the modality 30 are transmitted to the PACS 40. The modality 30 is an example of a medical imaging apparatus.

[0019] The PACS 40 is a computer system that receives medical images sent by the modality 30 and stores them in a database. The PACS 40 transmits (transfers) medical images stored in the database in response to requests from clients. The PACS 40 includes a server computer that includes a processor such as a CPU, memory such as ROM and RAM, a display, an input interface, and a communication interface. Medical images stored in the PACS 40 are accompanied by additional information such as information about the patient being imaged and the imaging process.

[0020] The incidental information includes, for example, information such as a patient ID, examination ID, imaging conditions, and imaging protocol in a format conforming to the DICOM (Digital Imaging and Communication in Medicine) standard. The incidental information may also include the manufacturer name of the modality 30 and imaging information at the time of imaging. Some or all of the incidental information is included in the examination information generated by the RIS 20 as categorized data.

[0021] The configuration of the in-hospital system 2 is not limited to the above. The in-hospital system 2 may include, for example, an image interpretation report creation device. Also, some elements of the in-hospital system 2 may be integrated. For example, the HIS 10 and the RIS 20 may be integrated into a single system.

[0022] The medical information processing device 100 is capable of communicating with the HIS 10, RIS 20, modality 30, and PACS 40 in the hospital system 2 via the network NW. Fig. 2 is a block diagram showing an example of the configuration of the medical information processing device 100 of the first embodiment. The medical information processing device 100 includes, for example, a communication interface 110, an input interface 120, a display 130, a processing circuit 140, and a memory 150.

[0023] The communication interface 110, input interface 120, and display 130 in the medical information processing device 100 are provided separately from the communication interface, input interface, and display provided in the HIS 10, but these may be common.

[0024] The communication interface 110 communicates with external devices such as the HIS 10, the RIS 20, the modality 30, and the PACS 40 via the network NW. The communication interface 110 includes a communication interface such as a network interface card (NIC). The communication interface 110 receives, for example, a recommendation request transmitted by the RIS 20 and examination information added to the recommendation request.

[0025] The input interface 120 receives various input operations from a doctor or the like, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuitry 140. The input interface 120 includes, for example, a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch panel, and the like.

[0026] The input interface 120 may be, for example, a user interface that accepts audio input from a microphone, etc. If the input interface 120 is a touch panel, the input interface 120 may also have the display function of the display 130.

[0027] In this specification, the input interface is not limited to an interface having physical operation parts such as a mouse, keyboard, etc. For example, an example of an input interface also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to a control circuit.

[0028] The display 130 displays various types of information. For example, the display 130 displays images acquired by the processing circuit 140, a GUI (Graphical User Interface) for receiving various input operations from an operator, etc. For example, the display 130 is an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, an organic EL (Electro Luminescence) display, etc.

[0029] The processing circuitry 140 includes, for example, an acquisition function 141, a reception function 142, a calculation function 143, a selection function 144, and a proposal function 145. The processing circuitry 140 realizes these functions by, for example, a hardware processor (computer) executing a program stored in a memory (storage circuitry) 150.

[0030] The hardware processor refers to a circuit such as a CPU, a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD) or a complex programmable logic device (CPLD)), or a field programmable gate array (FPGA).

[0031] Instead of storing the program in the memory 150, the program may be directly embedded in the circuit of the hardware processor. In this case, the hardware processor realizes its functions by reading and executing the program embedded in the circuit. The program may be stored in the memory 150 in advance, or may be stored in a non-transitory storage medium such as a DVD or CD-ROM, and installed into the memory 150 from the non-transitory storage medium by inserting the non-transitory storage medium into a drive device (not shown) of the medical information processing device 100.

[0032] The hardware processor is not limited to being configured as a single circuit, but may be configured as a single hardware processor by combining multiple independent circuits to realize each function. Furthermore, multiple components may be integrated into a single hardware processor to realize each function. The hardware processor, memory, etc. in the medical information processing device 100 are provided separately from the hardware processor, memory, etc. of the HIS 10, but they may be shared.

[0033] The memory 150 stores a medical application group 151 and a medical application database (hereinafter referred to as DB) 152. Fig. 3 is a diagram showing an example of the contents of the medical application group 151. The medical application group 151 includes multiple medical applications. The medical applications are given names such as "App-A," "App-B," "App-C," etc.

[0034] The medical app is an application used to analyze examinations using medical image data obtained by imaging a subject using the modality 30, and includes, for example, a trained model. The trained model is, for example, a model generated by performing machine learning using categorized data as training data.

[0035] The medical application DB 152 is a collection of data indicating the correspondence between medical applications included in the medical application group 151 and categorization data. Here, the categorization data includes data used as training data when generating the medical applications. FIG. 4 is a diagram showing an example of the contents of the medical application DB 152. In the medical application DB 152 shown in FIG. 4, for example, the medical application information of the medical application "App-A" includes categorization data of "Company A Model-A1 Protocol A-1," "Company B Model-B1 Protocol B-1," and "Company C Model-C1 Protocol C-1" classified in the category of "Device Information," and categorization data of "Age: 10-70Y" classified in the category of "Patient Information."

[0036] The acquisition function 141 acquires test information including categorization data related to tests on a subject. The acquisition function 141 acquires test information transmitted, for example, by the RIS 20 of the in-hospital system 2 and received by the communication interface 110. The test information includes categorization data associated with the test on the subject, and is classified by category, similar to a medical application. The acquisition function 141 is an example of a first acquisition unit.

[0037] The reception function 142 receives a recommendation request for a target medical application from a user. For example, the reception function 142 receives a recommendation request for a target medical application from a user when the communication interface 110 receives the recommendation request transmitted by the RIS 20. The reception function 142 is an example of a reception unit.

[0038] The calculation function 143 calculates the degree of agreement between the data included in the examination information and the data included in the medical application information. For example, the calculation function 143 compares the categorization data included in the examination information with the categorization data of all the medical applications included in the medical application group 151. As a result of the comparison, the calculation function 143 calculates the degree of agreement between the categorization data of the examination information and each of the categorization data in all the medical applications. The calculation function 143 is an example of a calculation unit.

[0039] The selection function 144 selects a target medical app to be used for processing related to the examination from among multiple medical apps based on the categorization data. For example, the selection function 144 compares the degree of match for each medical app calculated by the calculation function 143, and selects, for example, the medical app with the highest degree of match as the target medical app based on the degree of match between the categorization data and the examination information. The selection function 144 is an example of a selection unit.

[0040] The suggestion function 145 suggests the target medical app selected by the selection function 144 to a user in the hospital system 2. The selection function 144 selects the target medical app when the reception function 142 has received a recommendation request. For example, when the reception function 142 has received a recommendation request, the suggestion function 145 suggests the target medical app selected by the selection function 144 to the user who sent the recommendation request.

[0041] Next, the processing of the medical information processing device 100 will be described. In the medical information processing device 100 of the first embodiment, processing for analyzing an examination and processing for updating medical apps included in the medical app DB 152 are performed. These processing will be described below. Fig. 5 is a flowchart showing an example of processing of the medical information processing device 100 of the first embodiment. The flowchart shown in Fig. 5 starts in response to an input operation on the input interface 120 by a user such as a doctor who analyzes an examination, for example.

[0042] First, the reception function 142 determines whether or not a recommendation request has been received (step S101). For example, the reception function 142 receives a recommendation request when the acquisition function 141 has received the recommendation request, and does not receive a recommendation request when the acquisition function 141 has not received the recommendation request.

[0043] If it is determined that a recommendation request has not been received, the reception function 142 repeats the process of step S101. If it is determined that a recommendation request has been received, the acquisition function 141 acquires examination information related to the examination (step S103). For example, the examination information may be transmitted by the modality 30 if it is held by the modality 30 before or after the start of the examination, or may be transmitted by the PACS 40 if it is stored in the PACS 40 after the end of the examination.

[0044] Next, the calculation function 143 reads out the medical application DB 152 stored in the memory 150 (step S105). Next, the calculation function 143 compares the categorization data included in the examination information acquired by the acquisition function 141 with the categorization data in each of the multiple medical applications included in the medical application group 151. The calculation function 143 calculates the degree of agreement of the categorization data for all medical applications in the examination information (step S107).

[0045] Next, the selection function 144 selects the medical app whose categorization data matches the examination information most closely as the target medical app (step S109). Next, the suggestion function 145 suggests the medical app selected by the selection function 144 to the user who sent the recommendation request (step S111). In this way, the medical information processing apparatus 100 ends the process shown in FIG. 5.

[0046] Next, the processing flow in the medical information processing device 100 will be described. Fig. 6 is a diagram for explaining the processing flow in the medical information processing device 100 of the first embodiment. For example, the medical information processing device 100 in cloud C stores a plurality of medical applications "App-A," "App-B," "App-C," "App-D," "App-E," and so on. The medical applications "App-A," "App-B," "App-C," "App-D," "App-E," and so on are associated with medical application information "MAD-A," "MAD-B," "MAD-C," "MAD-D," "MAD-E," and so on, respectively, as categorization data. In the following explanation, the medical apps "App-A," "App-B," "App-C," "App-D," "App-E," etc. may be collectively referred to as the medical app "App-X," and the medical app information "MAD-A," "MAD-B," "MAD-C," "MAD-D," "MAD-E," etc. may be collectively referred to as the medical app information "MAD-X."

[0047] In Hospital H, a plurality of medical image data captured by a modality 30 are stored in a PACS 40 in an in-hospital system 2. In the in-hospital system 2, target medical image data is acquired from the plurality of medical image data in the PACS 40 by a RIS 20 or the like, and transmitted to a medical information processing device 100. Examination information "INI" relating to the examination is added to the target medical image data.

[0048] 7 is a diagram showing an example of a comparison between categorization data of medical application information and examination information. The medical information processing device 100 calculates, in the processing circuitry 140, the degree of agreement between the categorization data included in the examination information “INI” added to the target medical image data transmitted by the in-hospital system 2 and the categorization data included in the medical application information “MAD-X.” The processing circuitry 140 selects the medical application information with the highest degree of agreement (here, the medical application “App-B” shown in FIG. 6) as the target medical application information.

[0049] The medical information processing apparatus 100 of the first embodiment selects a medical app to be used for processing related to an examination based on examination information obtained when an examination is performed on a subject and medical app information associated with each medical app, using the selection function 144. This makes it possible to select a medical app appropriate for analysis, etc.

[0050] (Second embodiment) Next, a medical information processing device according to a second embodiment will be described. FIG. 8 is a block diagram showing an example of the configuration of a medical information processing device 200 according to the second embodiment. The medical information processing device 200 according to the second embodiment differs from the first embodiment mainly in that the processing circuitry 140 includes an update function 146. Below, the medical information processing device 200 according to the second embodiment will be described, focusing on the differences from the first embodiment. In the following description, elements that are common to the first embodiment will be assigned the same reference numerals, and their description may be omitted.

[0051] The update function 146 updates the medical application information included in the medical application DB 152 based on the examination information acquired by the acquisition function 141. The update function 146 updates the medical application DB 152 by, for example, adding categorized data of examination information when an examination is actually performed at Hospital H to the medical application DB 152. The update function 146 is an example of an update unit.

[0052] Next, a description will be given of the processing of the medical information processing device 200 of the second embodiment. Fig. 9 is a flowchart showing an example of the processing of the medical information processing device 200 of the second embodiment. In the medical information processing device 200 of the second embodiment, similarly to the medical information processing device 100 of the first embodiment, first, the reception function 142 determines whether or not a recommendation request has been received (step S201).

[0053] If it is determined that a recommendation request has not been received, the reception function 142 repeats the process of step S101. If it is determined that a recommendation request has been received, the calculation function 143 reads out the medical application DB 152 (step S205). Next, the calculation function 143 calculates the degree of coincidence of the categorization data for all medical applications in the examination information (step S207), and the selection function 144 selects the medical application whose categorization data has the highest degree of coincidence with the examination information as the target medical application (step S209).

[0054] The calculation function 143 may determine whether the medical application has been updated by the update function 146, and if the medical application has been updated, may calculate the degree of coincidence of the categorization data. In this case, the selection function 144 may select the target medical application based on past selection results.

[0055] Next, the suggestion function 145 suggests the medical app selected by the selection function 144 to the user who sent the recommendation request (step S211). Up to this point, the process is the same as in the first embodiment. In the medical information processing device 100 of the second embodiment, the update function 146 further updates the medical app information stored in the memory 150 based on the categorization data included in the examination information (step S215). In this way, the medical information processing device 200 ends the process shown in FIG. 9.

[0056] 10 is a diagram illustrating an example of updating medical app information based on categorization data of examination information. The medical information processing device 100, in the processing circuitry 140, acquires categorization data included in the examination information “INI” added to the target medical image data transmitted by the in-hospital system 2. The processing circuitry 140 updates the medical app information based on the acquired target medical image data, and updates the medical app DB 152 by including the updated medical app information in the medical app DB 152.

[0057] The medical information processing device 200 of the second embodiment has the same effects as the medical information processing device 100 of the first embodiment. Furthermore, the medical information processing device 200 of the second embodiment updates the medical application DB 152 stored in the memory 150 with categorized data based on examination information of examinations performed in a hospital. Therefore, the medical application DB 152 can be accurately updated based on actual examination results.

[0058] According to at least one of the embodiments described above, the medical information processing device includes an acquisition unit that acquires test information including categorization data related to the test of a subject, and a selection unit that selects a target medical app to be used for processing related to the test from among multiple medical apps based on the categorization data, thereby making it possible to select an appropriate medical app.

[0059] Although several embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0060] 1. Medical information processing system 2. In-hospital system 10...Hospital Information System (HIS) 20...Radiology Information System (RIS) 30...Medical imaging diagnostic equipment (modality) 40...Picture Archiving and Communication System (PACS) 100...Medical information processing device 110...Communication interface 120...input interface 130...Display 140...Processing circuit 150...Memory 151...Medical apps 152...Medical App DB NW...Network

Claims

1. an acquisition unit that acquires examination information including categorized data related to the examination of the subject; a selection unit that selects a target medical application to be used for the process related to the examination from among a plurality of medical applications based on the categorization data. Medical information processing equipment.

2. The medical application is associated with the categorization data, the selection unit selects the target medical application based on the categorization data included in the examination information and the categorization data associated with the medical application; The medical information processing device according to claim 1 .

3. a receiving unit that receives a recommendation request for the target medical app from a user; The selection unit selects the target medical app based on the recommendation request. The medical information processing device according to claim 1 .

4. the examination is an examination performed by photographing the subject with a medical imaging device, The medical application is an application used for an examination using medical images captured by the medical imaging device. The medical information processing device according to claim 1 .

5. the categorized data includes at least one of the following data: a manufacturer name of the medical imaging device, imaging information at the time of imaging, subject information about the subject, and findings of the examination; The medical information processing device according to claim 4 .

6. The imaging information includes at least one of imaging conditions, examination protocols, and order information. The medical information processing device according to claim 5 .

7. The subject information includes at least one of the subject's sex, age, race, height, and weight. The medical information processing device according to claim 5 .

8. The medical information processing device further includes a calculation unit that calculates a degree of coincidence between categorization data included in the examination information and categorization data associated with the medical application, the selection unit selects the medical application based on the calculated degree of coincidence. The medical information processing device according to claim 1 .

9. an update unit that updates the categorization data associated with the medical application based on the acquired examination information; The medical information processing device according to claim 1 .

10. the calculation unit calculates the degree of coincidence of the categorization data when at least one of the plurality of medical applications is updated; The medical information processing device according to claim 8 .

11. The medical application includes a trained model obtained by machine learning using at least a portion of the categorized data as training data. The medical information processing device according to claim 1 .

12. The computer obtaining test information including categorical data regarding tests on a subject; selecting a target medical app to be used for the process related to the examination from among a plurality of medical apps based on the categorization data; Medical information processing method.

13. On the computer, obtaining test information including categorical data regarding tests on a subject; selecting a target medical application to be used for the processing related to the examination from among a plurality of medical applications based on the categorization data; program.

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