JP6997981B2 - Diagnostic support device, diagnostic support method and diagnostic support program - Google Patents

Description

The present invention relates to a diagnostic support device, a diagnostic support method and a diagnostic support program, and particularly supports a patient's diagnosis in cooperation with an analysis system that analyzes a patient's disease based on the patient's medical image and the opinion of the diagnostician. , Diagnostic support device, Diagnostic support method and Diagnostic support program.

In a medical facility, an image interpreter analyzes a plurality of medical images output from a modality that photographs a patient and creates an image interpretation report. A medical image showing the lesion is attached to the interpretation report, and the name of the disease diagnosed from the attached medical image and the treatment method are described.

In order to improve the diagnostic accuracy of the image interpreter, that is, the reliability of the image interpretation report, it is necessary to accurately perform both "presence diagnosis" for diagnosing the presence or absence of a lesion and "qualitative diagnosis" for diagnosing the nature of the lesion. However, due to the improvement in the performance of CT devices and MRI devices in recent years, the number of examinations and the number of images have increased, which also increases the burden on the image interpreting doctor. Since such an increase in burden causes oversight of lesions, typographical errors, forgetting to write, etc., attention is paid to how to support the diagnosis of the image interpreter.

In relation to this, Patent Document 1 detects abnormal shadows and searches for similar cases based on image data to be diagnosed and its pathological condition information, and displays disease candidate names with high possibility. The output system is disclosed.

Further, in Patent Document 2, when a plurality of medical condition information is input, points are calculated for each disease name using a point map, and the disease names are associated with the disease names in descending order of the points. It discloses a medical diagnosis support device that displays images.

Japanese Patent No. 4087640 Japanese Unexamined Patent Publication No. 2009-59381

Both Patent Documents 1 and 2 attempt to perform image diagnosis by coordinating an analysis system that analyzes a patient's disease based on image data and / pathological information of a diagnosis target. Here, in order to increase the dependence of the analysis system in image diagnosis, it is required to improve the diagnostic accuracy by the analysis system. However, the design of the analysis system for improving the diagnostic accuracy may induce misdiagnosis and eventually reduce the diagnostic support performance.

Therefore, a main object of the present invention is to provide a diagnostic support device capable of enhancing support performance.

The diagnostic support device according to the present invention (10: reference code corresponding to the embodiment; the same applies hereinafter) is a diagnostic support device that supports diagnosis based on a plurality of medical images created for a common patient and a request statement. Therefore, the medical image output means (S9, S25 to S27) that outputs at least one of a plurality of medical images to the diagnostician, and the diagnostician's opinion on the request sentence is acquired in relation to the processing of the medical image output means. Acquisition means (S11 to S17), first analysis requesting means (S41, S45) that requires the analyzer (40) to perform a first analysis process for analyzing a patient's disease based on image information including a plurality of medical images. A second analysis requesting means that requests an analyzer to perform a second analysis process for analyzing a patient's disease based on textual information including a request statement and an opinion acquired by the acquisition means in connection with the processing of the first analysis requesting means. (S39, S43), and an analysis result output means (S47, S49) for outputting the first analysis result obtained by the first analysis process and the second analysis result obtained by the second analysis process are provided.

Preferably, the first analysis process is executed based only on the former of the image information and the character information, and the second analysis process is executed based only on the latter of the image information and the character information.

Preferably, the analysis result output means outputs the first analysis result and the second analysis result to the diagnostician at the same time.

Preferably, the medical image output means includes an update means (S25, S27) that updates the output target in response to the update operation, and the analysis result output means updates and acquires after the output of the first analysis result and the second analysis result. Restart at least one of the means.

Preferably, the interpretation report display means (S7) for displaying the interpretation report having the request text column (REQ) and the opinion entry column (RMK, DGN) on the first monitor (12m1) is further provided, and the acquisition means is the opinion opinion. Includes opinion display means (S13, S17) to be displayed in the entry field.

In some aspects, the medical image output means include medical image display means (S9, S27) that display at least one of the plurality of medical images on a second monitor (12 m2) attached to the first monitor.

In another aspect, the analysis result output means includes an additional display means (S49) for additionally displaying the first analysis result and the second analysis result on the first monitor.

Preferably, each of the first analysis requesting means and the second analysis requesting means issues an analysis request in a different manner depending on the attributes of the patient.

Preferably, each of the first analysis requesting means and the second analysis requesting means issues an analysis request to the analyzer when the request issuing operation is accepted.

Preferably, each of the first analysis result and the second analysis result includes a plurality of candidate disease names and the corresponding probability of each disease candidate.

More preferably, there is further an emphasis means (S57) that emphasizes the candidate disease name that satisfies the predetermined condition among the plurality of candidate disease names, and the omission condition that the default condition is omitted from the view acquired by the acquisition means. Includes the probability condition that the hit probability exceeds the standard.

The diagnostic support method according to the present invention is a diagnostic support method executed by a diagnostic support device (10) that supports a diagnosis based on a plurality of medical images created for a common patient and a request statement. Medical image output step (S9, S25 to S27) to output at least one of the medical images of S11 ~ S17), 1st analysis request step (S41, S45) requesting the analyzer (40) to analyze the patient's disease based on image information including multiple medical images, request text and acquisition A second analysis request step (S39, S43) that requests an analyzer to perform a second analysis process for analyzing a patient's disease based on textual information including the views acquired by the step in connection with the process of the first analysis request step. ), And an analysis result output step (S47, S49) for outputting the first analysis result obtained by the first analysis process and the second analysis result obtained by the second analysis process.

In the diagnostic support program according to the present invention, a plurality of medical images are mounted on a processor (12pr) of a diagnostic support device (10) that supports diagnosis based on a plurality of medical images created for a common patient and a request statement. A medical image output step (S9, S25 to S27) that outputs at least one to the diagnostician, and an acquisition step (S11 to S17) that acquires the diagnostician's opinion on the request sentence in relation to the processing of the medical image output step. , Acquired by the first analysis request step (S41, S45), request statement and acquisition step, which requires the analyzer (40) to perform the first analysis process to analyze the patient's disease based on image information including multiple medical images. The second analysis request step (S39, S43), which requests the analyzer to perform the second analysis process for analyzing the patient's disease based on the textual information including the opinion, in relation to the process of the first analysis request step, and the first. 1 This is a diagnostic support program for executing an analysis result output step (S47, S49) that outputs the first analysis result obtained by the analysis process and the second analysis result obtained by the second analysis process.

By targeting a plurality of medical images for the first analysis process, the concern that the medical image showing the lesion is overlooked and the diagnosis is made is reduced. In addition, by targeting the request text and the diagnostician's opinion on it in the second analysis process, diagnosis can be performed without paying attention to some diseases (that is, without noticing a part of the disease name). The concern is reduced. This improves the diagnostic support performance.

The above-mentioned object, other object, feature and advantage of the present invention will be further clarified from the detailed description of the following examples with reference to the drawings.

It is a block diagram which shows the structure of the medical diagnosis support system applied to this Example. It is a schematic diagram which shows an example of the structure of the table referred by a machine learning system. It is a schematic diagram which shows an example of the structure of the DICOM file created by a modality. It is a schematic diagram which shows an example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a schematic diagram which shows another example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a schematic diagram which shows an example of the diagnosis support screen displayed on the sub monitor of an image diagnosis support device. It is a schematic diagram which shows another example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a schematic diagram which shows the other example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a schematic diagram which shows another example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a schematic diagram which shows another example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a schematic diagram which shows the other example of the diagnosis support screen displayed on the main monitor of an image diagnosis support device. It is a flow diagram which shows a part of the operation of the CPU provided in the image diagnosis support apparatus. It is a flow diagram which shows the other part of the operation of the CPU provided in the image diagnosis support device. It is a flow diagram which shows the other part of the operation of the CPU provided in the image diagnosis support device. It is a flow diagram which shows the other part of the operation of the CPU provided in the image diagnosis support apparatus. It is a flow diagram which shows the other part of the operation of the CPU provided in the image diagnosis support device.

With reference to FIG. 1, the medical diagnosis support system of this embodiment is composed of an image diagnosis support device 10, a modality 20, a PACS 30, and a machine learning system (analysis device) 40 connected to each other via an in-hospital LAN 50.

In the image diagnosis support device 10, a communication I / F 12 cm, a CPU 12 pr, a keyboard / mouse 12 km, a DRAM 12 mm, an HDD 12 hd, a main monitor (first monitor) 12 m1 and a sub monitor (second monitor) 12 m 2 are connected to the bus BS1.

The sub monitor 12m2 is attached to the main monitor 12m1. The image interpreter (diagnostician) faces the main monitor 12m1 and the sub monitor 12m2, and operates the keyboard / mouse 12km to perform image diagnosis.

The machine learning system 40 is composed of a text-specialized machine learning system 40at for adults, an image-specialized machine learning system 40ai for adults, a text-specialized machine learning system 40ct for children, and an image-specialized machine learning system 40ci for children. .. The disease candidate table shown in FIG. 2 is provided in the machine learning system 40 so as to be accessible from any of the text-specialized machine learning systems 40at and 40ct and the image-specialized machine learning systems 40ai and 40ci.

According to FIG. 2, "lung cancer", "metastatic lung tumor", "mediastinal tumor", "pulmonary arterial thrombosis", "pulmonary edema", "chestitis", "pneumothorax", "pneumothorax", "chronic obstructive" Many character strings such as "lung disease", "pulmonary tuberculosis", "interstitial pneumonia", "sarcoidosis", "lung lung", "lymphangioleiomyomatosis", "acute respiratory failure", "bronchiectasis" are diseases. Registered in the candidate table.

Modality 20 is, for example, an MRI apparatus that outputs a DICOM file containing a plurality of medical images representing a cross section of a patient's body axis. The inspection engineer who operates the modality 20 operates the modality 20 based on the request comment from the attending physician. As a result, a tomographic image at a desired position is created as a medical image, and the created medical image is stored in a DICOM file. The DICOM file thus created has the data structure shown in FIG. 3 and is stored in the PACS 30 via the in-hospital LAN 50.

With reference to FIG. 3, the DICOM file contains a plurality of medical images as well as ancillary information. Here, the attached information is composed of a plurality of items (examination date, patient name, patient's age at the time of examination, accession number, etc.) indicating the examination attributes.

The CPU 12pr provided in the image diagnosis support device 10 executes the diagnosis support process shown in FIGS. 12 to 16 in order to support the image diagnosis by the image interpreting doctor in cooperation with the machine learning system 40. The program corresponding to this flow chart is stored in the HDD 12 hd as a diagnostic support program.

With reference to FIG. 12, in step S1, the top screen is expanded to the DRAM 12 mm. The expanded top screen is read by the main monitor 12m1 and displayed as shown in FIG. According to FIG. 4, the top screen has a heading column IDX in which a plurality of headings representing a plurality of tests are listed. Further, on the top screen shown in FIG. 4, the interpretation report corresponding to the heading is also "pending".

In step S3, it is determined whether or not the heading selection operation (= operation of selecting any one of the plurality of headings listed in the heading column IDX) is performed by the keyboard / mouse 12 km. Further, in step S5, it is determined whether or not another operation is performed by the keyboard / mouse 12 km. If both the discrimination result of step S3 and the discrimination result of step S5 are NO, the process returns to step S3, if the discrimination result of step S3 is YES, the process proceeds to step S7, and if the discrimination result of step S5 is YES, the other steps are taken. Proceed to the process of.

In step S7, the interpretation report screen corresponding to the heading selected by the heading selection operation is expanded on the DRAM 12 mm. The top screen on the DRAM 12 mm is updated by the interpretation report screen.

The updated interpretation report screen is read by the main monitor 12m1 and displayed as shown in FIG. According to FIG. 5, the image interpretation report screen is provided with a request comment column (request sentence column) RQ, a finding column RMK, a diagnosis column DGN, and an attached image column IMG. Of these, the character string of "chest pain" in the request comment column RQ is written by the attending physician. Further, in this embodiment, the finding column RMK and the diagnostic column DGN are collectively referred to as "opinion entry column", and the findings described in the finding column RMK and the diagnostic comment described in the diagnostic column DGN are collectively referred to as "opinion".

In step S9, the PACS 30 is accessed through the communication I / F 12 cm, and the DICOM file corresponding to the accession number assigned to the selected heading is acquired from the PACS 30. In step S9, any one of the plurality of medical images contained in the acquired DICOM file is expanded into the DRAM 12 mm. The developed medical image is read out by the sub monitor 12m2 and displayed as shown in FIG.

In step S11, it is determined whether or not the finding input operation (the operation of designating the finding column RMK and inputting a desired character string) is performed by the keyboard / mouse 12 km. If the determination result is NO, the process proceeds to step S15 as it is.

On the other hand, if the determination result is YES, the process proceeds to step S13, and the character string acquired by the finding input operation is displayed in the finding column RMK as shown in FIG. According to FIG. 7, in the finding column RMK, the character string “calcification is observed at the origin of the coronary artery” is displayed. When the display of the character string is completed, the process proceeds to step S15.

In step S15, it is determined whether or not the diagnostic comment input operation (the operation of designating the diagnostic field DGN and inputting a desired character string) is performed by the keyboard / mouse 12 km. If the determination result is NO, the process proceeds to step S19 as it is.

On the other hand, if the determination result is YES, the process proceeds to step S17, and the character string acquired by the diagnosis comment input operation is displayed in the diagnosis column DGN as shown in FIG. According to FIG. 8, the character string “1. Suspected angina” is displayed in the diagnosis column DGN. When the display of the character string is completed, the process proceeds to step S19.

In step S19, it is determined whether or not the image attachment operation (the operation of moving the medical image displayed on the sub monitor 12m2 to the attached image field IMG by the drag and drop operation) is performed by the keyboard / mouse 12 km. If the discrimination result is NO, the process proceeds to step S25 as it is, while if the discrimination result is YES, the following processes are executed in steps S21 and S23, and then the process proceeds to step S31.

That is, in step S21, a thumbnail image is created based on the medical image displayed on the sub monitor 12m2. In step S23, the created thumbnail image is displayed in the attached image field IMG (see FIGS. 7 and 8).

In step S25, it is determined whether or not the image update operation (the operation of updating the medical image on the sub monitor 12 m2) is performed by the keyboard / mouse 12 km. If the determination result is NO, the process proceeds to step S29 as it is.

On the other hand, if the discrimination result is YES, the process proceeds to step S27, and another medical image in the DICOM file acquired in step S9 is developed on the DRAM 12 mm. As a result, the medical image displayed on the sub monitor 12m2 is updated with another medical image. When the process of step S27 is completed, the process proceeds to step S29.

In step S29, it is determined whether or not the "confirmation" button (see FIGS. 7 and 8) displayed in the vicinity of the attached image field IMG is clicked by the keyboard / mouse 12 km (whether or not the request issuing operation is performed). do. If the determination result in step S29 is NO, it is determined in step S31 whether or not the "OK" button displayed in the upper right of the interpretation report screen is clicked by the keyboard / mouse 12 km, and another operation is performed by the keyboard / mouse 12 km. In step S35, it is determined whether or not the above has been performed.

If both the discrimination result in step S31 and the discrimination result in step S35 are NO, the process returns to step S11. If the discrimination result in step S35 is YES, the process proceeds to another process. If the discrimination result in step S31 is YES, the process proceeds to another process. After performing the confirmation process in step S33, the process returns to step S1.

As a result, the display of the main monitor 12m1 returns from the interpretation report screen to the top screen. However, as shown by thick circles in FIG. 9, the character string "confirmed" is added to the heading corresponding to the interpretation report that has undergone the definite process, instead of "pending".

If the determination result in step S29 is YES, it is determined in step S37 whether or not the patient's examination age described on the interpretation report screen is 15 years or older. If the discrimination result is YES, the process proceeds to step S39, while if the discrimination result is NO, the process proceeds to step S43.

In step S39, the candidate disease presentation request (analysis request) is transmitted to the text specialized machine learning system 40at, and in the following step S41, the candidate disease presentation request is transmitted to the image specialized machine learning system 40ai. Further, in step S43, the candidate disease presentation request is transmitted to the text specialized machine learning system 40ct, and in the following step S45, the candidate disease presentation request is transmitted to the image specialized machine learning system 40ci. Candidate disease presentation requests are issued via communication I / F 12 cm and in different embodiments depending on the patient's attributes.

The candidate disease presentation request transmitted in step S39 or S43 is accompanied by the same character string as the character string displayed in the request comment field EQU as character information representing the request comment, and is combined with the character string displayed in the finding field RMK. The same character string is attached as character information representing a finding, and the same character string as the character string displayed in the diagnosis field DGN is attached as character information representing a diagnosis comment.

Further, the candidate disease presentation request transmitted in step S41 or S45 is accompanied by all the medical images in the DICOM file acquired in step S9 as image information.

Each of the text-specialized machine learning systems 40at and 40ct has artificial intelligence similar to that of an experienced physician who has diagnosed a patient's disease based on request comments, findings and diagnostic comments. In addition, each of the image-specialized machine learning systems 40ai and 40ci has artificial intelligence similar to that of a doctor who has abundant experience in diagnosing a patient's disease based on medical images.

Each of the text-specialized machine learning systems 40at and 40ct analyzes the patient's disease based on the textual information accompanying the candidate disease presentation request. On the other hand, each of the image-specialized machine learning systems 40ai and 40ci analyzes the patient's disease based on the image information accompanying the candidate disease presentation request. That is, each of the text-specialized machine learning systems 40at and 40ct executes an analysis process ( second analysis process) based only on the former of the character information and the image information, and each of the image-specialized machine learning systems 40ai and 40ci The analysis process ( first analysis process) is executed based only on the latter of the character information and the image information.

Each of the text-specialized machine learning systems 40at and 40ct extracts a plurality of candidate diseases from the candidate disease table shown in FIG. 2 by analysis processing based on character information, and the extracted multiple disease candidates and the probability of corresponding to each disease candidate. The reply including is returned to the requester. Similarly, each of the image-specialized machine learning systems 40ai and 40ci also extracts a plurality of candidate diseases from the candidate disease table shown in FIG. 2 by analysis processing based on image information, and corresponds to the extracted multiple disease candidates and each disease candidate. Return the response, including the probability of doing so, to the requester.

When the processing of step S41 or S45 is completed, it is determined whether or not there is an answer from both the text-specialized machine learning system 40at and the image-specialized machine learning system 40ai or both the text-specialized machine learning system 40ct and the image-specialized machine learning system 40ci. Repeatedly determine with.

When the determination result is updated from NO to YES, the process proceeds to step S49, and the analysis result screen is expanded to the DRAM 12 mm. The expanded analysis result screen is read out by the main monitor 12m1 and is displayed multiple times (additionally displayed) on the image interpretation report screen as shown in FIG.

With reference to FIG. 10, the candidate disease name and the corresponding probability representing the analysis result based on the image information are displayed on the left side of the analysis result screen, and the candidate disease name and the corresponding probability representing the analysis result based on the text information are displayed on the analysis result screen. It is displayed on the right side. In addition, the analysis result based on the image information and the analysis result based on the character information are displayed at the same time.

Here, the analysis results based on the image information are arranged in the order of "pleurisy 80%", "angina 50%", "acute myocarditis 15%", and "lung cancer 10%". The analysis results based on the textual information are arranged in the order of "angina 80%", "myocardial infarction 40%", "aortic valve disease 30%", and "acute myocarditis 10%".

In step S51, the name of the candidate disease leaked from the diagnosis column DGN is searched from the analysis result screen, and in step S53, it is determined whether or not the search result indicates "detection". If the determination result is NO, it is considered that the leakage condition is not satisfied, and the process returns to step S11. On the other hand, if the determination result is YES, it is considered that the leakage condition is satisfied, and the process proceeds to step S55.

In step S55, it is determined whether or not the corresponding probability of the detected candidate disease name exceeds the reference value (= 70%). If the determination result is NO, it is considered that the probability condition is not satisfied, and the process returns to step S11. On the other hand, if the determination result is YES, it is considered that the probability condition is satisfied, and the process proceeds to step S57. In step S57, the detected disease candidate names and applicable probabilities are highlighted in bold frames for alerts. When the enhancement process is completed, the process returns to step S11.

According to FIG. 10, "pleurisy" is leaking from the diagnostic column DGN, while "angina" is described in the diagnostic column DGN. Therefore, of "pleurisy 80%" and "angina 80%", only "pleurisy 80%" is emphasized in a thick frame.

By subjecting all the medical images contained in the DICOM file to the analysis processing by the image specialized machine learning system 40ai or 40ci, the concern that the medical image showing the lesion is overlooked and the diagnosis is made is reduced. In addition, by targeting request comments, findings, and diagnostic comments for analysis processing by the text-specialized machine learning system 40at or 40ct, without paying attention to some diseases (that is, without noticing a part of the disease name). ) The concern that a diagnosis will be made is reduced.

By returning to step S11 after the display of the analysis result screen is completed, the processes from step S11 to step S27 are restarted, and the character string displayed in each of the finding column RMK and the diagnostic column DGN and the attached image column IMG are displayed. The displayed thumbnail image can be edited (see FIG. 11).

According to the description in the thick frame shown in FIG. 11, the character string "Inflammatory findings are found in the left pleura" is added to the finding column RMK, and the description in the diagnostic column DGN is "1. Pleurisy" and "2. Narrow". It is changed to "suspicion of pleurisy". When the "confirmation" button is clicked after editing, the processes from step S37 to step S57 are restarted, and the analysis result screen is updated.

As can be seen from the above description, the diagnostic imaging support device 10 is a device that supports diagnosis based on a plurality of medical images created for a common patient and request comments, and is described by the CPU 12pr constituting the device. The process is executed.

First, at least one of the plurality of medical images is displayed on the sub monitor 12m2 facing the image interpreter (S9, S25 to S27). In addition, the opinion of the image interpreter (findings and / or diagnostic comment) regarding the request comment is acquired through the keyboard / mouse 12 km and displayed on the image interpretation report screen on the main monitor 12 m1 (S11 to S17).

Subsequently, a candidate disease presentation request accompanied by a plurality of medical images is issued to the image specialized machine learning system 40ai or 40ci (S41, S45), and a candidate disease presentation request accompanied by a request comment and an opinion is a text specialized machine. Issued for learning systems 40at or 40ct (S39, S43). The former candidate disease presentation request requests the analysis of the patient's disease based on the image information, and the latter candidate disease presentation request requests the analysis of the patient's disease based on the text information.

The analysis result by the image specialized machine learning system 40ai or 40ci and the analysis result by the text specialized machine learning system 40at or 40ct are displayed in multiple layers on the interpretation report screen on the main monitor 12m1 (S47, S49).

By targeting a plurality of medical images for analysis processing by the image specialized machine learning system 40ai or 40ci, the concern that the medical image showing the lesion is overlooked and the diagnosis is made is reduced. In addition, by subjecting the request comment and the opinion of the image interpreter to the analysis processing by the text-specialized machine learning system 40at or 40ct, it is possible to make a part of the disease name without paying attention to some diseases (that is, a part of the disease name). The concern that a diagnosis will be made (without being noticed) is reduced. As a result, even an inexperienced image interpreter can accurately perform diagnostic imaging.

In this embodiment, the attributes of the patient are classified into two, "adult" and "child". However, the attributes of the patient may be classified into two categories, "male" and "female", or may be classified into four categories: "adult male", "adult female", "pediatric male", and "pediatric female". , And even more diverse.

Further, in this embodiment, when the "confirmation" button is clicked, the diagnostic imaging support device 10 transmits a candidate disease presentation request to the image specialized machine learning system 40ai or 40ci (step S41 in FIG. 15). See S45). Further, the image-specialized machine learning system 40ai or 40ci analyzes the patient's disease based on the image information accompanying the transmitted candidate disease presentation request, and returns the analysis result to the image diagnosis support device 10.

However, instead of this, when the DICOM file is saved in the PACS 30, the PACS 30 accompanies an analysis request (all medical images and DICOM file numbers in the DICOM file) to the image specialist machine learning system 40ai or 40ci. ), And when the "Confirm" button is clicked, the diagnostic imaging support device 10 sends a candidate disease presentation request (with a DICOM file number) to the image specialized machine learning system 40ai or 40ci. You may.

In this case, the image-specialized machine learning system 40ai or 40ci analyzes the patient's disease based on the image information accompanying the analysis request, and returns the analysis result to the image diagnosis support device 10 in response to the candidate disease presentation request.

This allows the image specialist machine learning system 40ai or 40ci to analyze the patient's disease before the "confirm" button is clicked. As a result, the response characteristics to the click of the "confirmation" button are improved.

When the analysis request is transmitted from the PACS 30 to the image specialized machine learning system 40ai or 40ci, the device configured by the diagnostic imaging support device 10 and the PACS 30 corresponds to the "diagnosis support device" of the present invention.

10 ... Image diagnosis support device 12pr ... CPU
12km ... keyboard / mouse 12hd ... HDD
12m1 ... Main monitor 12m2 ... Sub monitor 40 ... Machine learning system 40at, 40ct ... Text specialized machine learning system 40ai, 40ci ... Image specialized machine learning system

Claims (12)

It is a diagnostic support device that supports diagnosis based on multiple medical images created for a common patient and a request statement.
A medical image output means that outputs at least one of the plurality of medical images to a diagnostician.
An acquisition means for acquiring the diagnostician's opinion on the request statement in connection with the processing of the medical image output means,
A first analysis requesting means for requesting an analyzer to perform a first analysis process for analyzing a disease of the patient based on image information including the plurality of medical images.
The analysis device is requested to perform a second analysis process for analyzing the disease of the patient based on the character information including the request sentence and the opinion acquired by the acquisition means in relation to the process of the first analysis request means. A second analysis requesting means, and an analysis result output means for simultaneously outputting the first analysis result obtained by the first analysis process and the second analysis result obtained by the second analysis process to the diagnostician . A diagnostic support device to prepare. The first analysis process is executed based only on the former of the image information and the character information.
The diagnostic support device according to claim 1, wherein the second analysis process is executed based only on the latter of the image information and the character information. The medical image output means includes an update means for updating an output target in response to an update operation.
The diagnostic support device according to claim 1 or 2 , wherein the analysis result output means restarts at least one of the update means and the acquisition means after the output of the first analysis result and the second analysis result. Further provided with an interpretation report display means for displaying an interpretation report having a request text column and an opinion entry column on the first monitor.
The diagnostic support device according to any one of claims 1 to 3 , wherein the acquisition means includes an opinion display means for displaying the opinion in the opinion entry field. The diagnostic support device according to claim 4 , wherein the medical image output means includes a medical image display means for displaying at least one of the plurality of medical images on a second monitor attached to the first monitor. The diagnostic support device according to claim 4 or 5 , wherein the analysis result output means includes an additional display means for additionally displaying the first analysis result and the second analysis result on the first monitor. The diagnostic support device according to any one of claims 1 to 6 , wherein each of the first analysis requesting means and the second analysis requesting means issues an analysis request in a different manner according to the attribute of the patient. The diagnostic support device according to any one of claims 1 to 7 , wherein each of the first analysis requesting means and the second analysis requesting means issues an analysis request to the analysis device when the request issuing operation is received. The diagnostic support device according to any one of claims 1 to 8 , wherein each of the first analysis result and the second analysis result includes a plurality of candidate disease names and corresponding probabilities of each disease candidate. Further provided with an emphasis means for emphasizing the candidate disease name satisfying the predetermined condition among the plurality of candidate disease names.
The diagnostic support device according to claim 9 , wherein the default condition includes a leakage condition that the view acquired by the acquisition means is omitted and a probability condition that the corresponding probability exceeds the standard. It is a diagnostic support method executed by the processor of a diagnostic support device that supports diagnosis based on multiple medical images created for a common patient and a request statement.
A medical image output step that outputs at least one of the plurality of medical images to a diagnostician.
An acquisition step of acquiring the diagnostician's view of the request statement in connection with the processing of the medical image output step,
A first analysis request step that requires an analyzer to perform a first analysis process for analyzing a disease of the patient based on image information including the plurality of medical images.
The analysis device is requested to perform a second analysis process for analyzing the patient's disease based on the character information including the request statement and the opinion acquired by the acquisition step in connection with the process of the first analysis request step. A second analysis request step, and an analysis result output step for simultaneously outputting the first analysis result obtained by the first analysis process and the second analysis result obtained by the second analysis process to the diagnostician . Diagnostic support method to prepare. To the processor of the diagnostic support device that supports the diagnosis based on multiple medical images created for a common patient and the request text.
A medical image output step that outputs at least one of the plurality of medical images to a diagnostician.
An acquisition step of acquiring the diagnostician's view of the request statement in connection with the processing of the medical image output step,
A first analysis request step that requires an analyzer to perform a first analysis process for analyzing a disease of the patient based on image information including the plurality of medical images.
The analysis device is requested to perform a second analysis process for analyzing the patient's disease based on the character information including the request statement and the opinion acquired by the acquisition step in connection with the process of the first analysis request step. A second analysis request step, and an analysis result output step for simultaneously outputting the first analysis result obtained by the first analysis process and the second analysis result obtained by the second analysis process to the diagnostician . A diagnostic support program to be executed.

Images