Computer program and method

Abstract

To provide a computer program and a method for medical image analysis based on artificial intelligence.SOLUTION: A computer program stored on a computer-readable storage medium includes instructions that, when executed by a processor, cause the processor to provide a viewer, wherein the viewer displays a worklist including an image list for image interpretation in conjunction with an image storage device that stores analysis results for medical images of a medical institution, and distinguishably displays medical images classified as normal cases in the worklist based on whether a normal case is included in the analysis results for the medical images.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to artificial intelligence-based medical image analysis technology. [Background technology]

[0002] As artificial intelligence (AI) technology has been actively introduced into the medical field in recent years, AI-based medical image analysis technology, such as the Lunit INSIGHT solution, is being researched, in which AI analyzes medical images and provides the analysis results visually.

[0003] Interpreters are assigned image interpretation tasks through a worklist, and after checking for abnormalities provided by medical image analysis, they can perform image interpretation tasks by creating interpretation reports. Interpreters can rearrange the worklist to change the image interpretation order, allowing them to prioritize images that require urgent interpretation or images with detected abnormalities. However, rearranging the worklist only changes the interpretation order of images already included in the worklist; normal images must still be interpreted eventually, so the interpretation workload remains unchanged. While the proportion of normal images among all images varies depending on the country, hospital size, and clinical environment, the proportion of normal images without detected lesions is generally significant, so a method to improve interpretation efficiency is needed. Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides an artificial intelligence-based medical image analysis method and system.

[0005] The present disclosure relates to an interface screen that provides artificial intelligence-based medical image analysis results.

[0006] The present disclosure relates to a method for providing a worklist using the results of medical image analysis based on artificial intelligence.

[0007] The present disclosure relates to a method for generating interpretation sentences using the results of medical image analysis based on artificial intelligence. [Means for solving the problem]

[0008] According to an embodiment, the image analysis device includes a memory and a processor for executing instructions stored in the memory. The processor obtains an analysis result for an input medical image using a first artificial intelligence model and a second artificial intelligence model trained to analyze medical images for different tasks, and classifies the input medical image as a normal case when the first artificial intelligence model and the second artificial intelligence model determine that all of the input medical images are normal.

[0009] The first artificial intelligence model may be a normal filtering model trained to comprehensively detect abnormal findings in medical images using more conservative criteria than the second artificial intelligence model, and the second artificial intelligence model may be a model trained to detect specific lesions from medical images.

[0010] The processor can determine whether the input medical image is normal or not based on the non-normal score obtained from the analysis results of the first artificial intelligence model and the normal filtering threshold.

[0011] The processor can change the current normality filtering threshold to a new value in response to user input.

[0012] The processor may determine a new normal filtering threshold for normal filtering based on at least one of the type of medical institution analyzing the input medical image, a distribution map of normal cases or a distribution map of abnormal cases of the medical institution relative to the abnormal score, and may suggest the new normal filtering threshold to the user.

[0013] The processor can determine whether the input medical image is normal or not based on the lesion-specific scores obtained from the analysis results of the second artificial intelligence model.

[0014] The processor can classify the input medical image as an abnormal case if the first artificial intelligence model determines that the input medical image is non-normal and the second artificial intelligence model determines that the input medical image is normal or abnormal.

[0015] The processor can generate a report of the input medical image classified as a normal case using a normal finding statement preset for the normal case.

[0016] The processor provides the analysis results for the input medical image as a secondary capture (SC) in DICOM format, and the SC may include a graphical indicator that indicates that the input medical image is a normal case.

[0017] An operating method of an image analysis device according to one embodiment includes obtaining an analysis result for an input medical image using a first artificial intelligence model and a second artificial intelligence model trained to analyze medical images for different tasks, classifying the input medical image as a normal case if the first artificial intelligence model and the second artificial intelligence model determine that all of the input medical images are normal, and providing the analysis result for the input medical image and a final analysis result including whether or not there are any normal cases in the input medical image to a designated device.

[0018] The first artificial intelligence model may be a normal filtering model trained to comprehensively detect abnormal findings in medical images using more conservative criteria than the second artificial intelligence model, and the second artificial intelligence model may be a model trained to detect specific lesions in medical images.

[0019] Classifying input medical images into normal cases can be done by determining whether the input medical images are normal or not based on the non-normal score obtained from the analysis results of the first artificial intelligence model and the normal filtering threshold, and by determining whether the input medical images are normal or not based on the lesion-specific score obtained from the analysis results of the second artificial intelligence model.

[0020] The method of operation may further include changing the current normal filtering threshold to a new value in response to user input.

[0021] The operating method may further include determining a new normal filtering threshold for normal filtering based on at least one of a type of medical institution analyzing the input medical image, a distribution map of normal cases or a distribution map of abnormal cases of the medical institution relative to the abnormal score, and suggesting the new normal filtering threshold to a user.

[0022] The operating method may further include classifying the input medical image as a non-normal case if the first artificial intelligence model determines that the input medical image is non-normal and the second artificial intelligence model determines that the input medical image is normal or non-normal.

[0023] The method may further include generating an interpretation of the input medical image classified as a normal case using a normal finding expression preset for the normal case.

[0024] The final analysis result for the input medical image is provided as a secondary capture (SC) in DICOM format, and the SC can include a graphical indicator that indicates that the input medical image is a normal case.

[0025] According to one embodiment, a computer program stored in a computer-readable storage medium includes instructions to cause a processor to execute the program in conjunction with an image storage device that stores analysis results of medical images to display a worklist including an image list for image interpretation work, and to distinguish and display medical images classified as normal cases from the worklist based on the presence or absence of normal cases included in the analysis results of the medical images.

[0026] The worklist may further include a command to display a secondary capture (SC) image showing the analysis result of the specific medical image when the specific medical image classified as a normal case is selected. The SC image may include a graphical indicator indicating that the specific medical image is a normal case.

[0027] The computer program further includes instructions for providing an interpretation sentence generated for the medical image classified as a normal case, and the interpretation sentence for the medical image classified as a normal case can be automatically generated using a normal finding sentence preset for the normal case. [Effects of the Invention]

[0028] According to one embodiment, medical images are analyzed using artificial intelligence models trained to analyze medical images for different tasks, thereby improving the accuracy and reliability of the analysis results.

[0029] According to one embodiment, by classifying normal medical images with high reliability among medical images and providing a graphical indicator that allows recognition of normal cases, interpretation time and interpretation workload can be reduced, improving interpretation efficiency, and as a result, reducing memory resources and computing resources of the medical imaging system for managing medical images waiting for interpretation.

[0030] According to one embodiment, by classifying normal medical images with high reliability among medical images, medical institutions that use the images can reduce interpretation costs.

[0031] According to one embodiment, by automatically generating interpretation text based on the analysis results of medical images, interpretation time and workload can be reduced, improving interpretation efficiency, and as a result, reducing memory resources and computing resources of the medical imaging system for managing medical images waiting for interpretation.

[0032] According to one embodiment, by assigning the interpretation work for normal cases to less skilled users or to remote interpretation technicians, and efficiently operating in-hospital interpretation work, it is possible to reduce the memory resources and computing resources of the medical imaging system for managing medical images waiting for interpretation.

[0033] According to one embodiment, a user can identify normal cases from a worklist containing a list of images for video interpretation work and determine the order of work, and can set priorities to read abnormal cases before normal cases.

[0034] According to one embodiment, a user can spend more time on abnormal cases than on normal cases, and can reduce the time required to interpret normal cases by using SC (Secondary Capture) images and GSPS (Grayscale Softcopy Presentation State) that include an indication that the case is normal. [Brief explanation of the drawings]

[0035] [Figure 1] 1 is a configuration diagram of a medical imaging system according to an embodiment. [Figure 2] 1 is a diagram illustrating a method for classifying medical images into normal cases and abnormal cases according to an embodiment; [Figure 3] 10 is an example of an auxiliary image providing analysis results according to one embodiment. [Figure 4] 10 is an example of an auxiliary image providing analysis results according to one embodiment. [Figure 5]10 is an example of an auxiliary image providing analysis results according to one embodiment. [Figure 6] 10 is a diagram illustrating a method for setting a threshold for normality filtering according to an embodiment; [Figure 7] 1 is an illustration of a worklist generated according to one embodiment. [Figure 8] 10 is an example of a deciphered sentence generated according to one embodiment. [Figure 9] 1 is a flowchart of a video analysis method according to an embodiment. [Figure 10] 1 is a flowchart of a method for setting a threshold for normality filtering according to one embodiment. [Figure 11] 1 is a flowchart of a method for providing analysis results according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0036] Hereinafter, with reference to the accompanying drawings, embodiments of the present disclosure will be described in detail so that those skilled in the art can easily carry out the present disclosure. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. In addition, to clearly explain the present disclosure in the drawings, parts that are not relevant to the description are omitted, and similar parts are designated by similar reference numerals throughout the specification.

[0037] In the description, when a part "includes" a certain component, it does not mean that it excludes other components, but that it may further include other components, unless otherwise specified. Furthermore, terms such as "... unit," "... machine," and "module" used in the specification refer to a unit that processes at least one function or operation, which can be realized by hardware, software, or a combination of hardware and software.

[0038] An apparatus or terminal of the present disclosure is a computing device configured and coupled to perform the operations of the present disclosure by executing instructions on at least one processor. A computer program is a program stored on a non-transitory computer-readable storage medium that includes instructions written to cause a processor to perform the operations of the present disclosure. The computer program may be stored on a readable storage medium. The computer program may be downloaded over a network and / or sold in product form.

[0039] The medical images disclosed herein may be images of various parts of the body taken using various modalities, such as X-ray, MRI (magnetic resonance imaging), ultrasound, CT (computed tomography), digital MMG (mammography), and DBT (digital breast tomosynthesis).

[0040] A user of the present disclosure is a medical professional, such as, but not limited to, a doctor, nurse, clinical pathologist, sonographer, or radiologist.

[0041] The artificial intelligence model (AI model) of the present disclosure is a machine learning model that learns at least one task and can be implemented as a computer program executed by a processor. The task learned by the AI ​​model can refer to a problem to be solved or an operation to be performed through machine learning. The AI ​​model can be implemented as a computer program executed on a computing device, downloaded via a network, and / or sold in the form of a product. The AI ​​model can also interface with various devices via a network.

[0042] FIG. 1 is a configuration diagram of a medical imaging system according to an embodiment, FIG. 2 is a diagram illustrating a method for classifying medical images into normal cases and abnormal cases according to an embodiment, and each of FIGS. 3 to 5 is an example of an auxiliary image that provides an analysis result according to an embodiment.

[0043] Referring to FIG. 1, a medical imaging system 1 may include at least one user terminal 100, an image storage device 200, and an image analysis device 300.

[0044] The user terminal 100 includes hardware and software that installs a program executed by a processor and provides a computing environment and a network environment for performing the operations of the present disclosure. The user terminal 100 can be implemented in various types, such as a computing device in a workstation or a mobile device. The user terminal 100 can include a viewer (simply referred to as "viewer") 110 that interfaces with the image storage device 200 and displays medical image-related data stored in the image storage device 200. The viewer 110 is installed and executed in a computing device in a workstation, for example, and is implemented to connect to the image storage device 200 and display medical image-related data stored in the image storage device 200. The viewer 110 is a computer program stored in a computer-readable medium and includes instructions that can be executed by a processor. The processor of the user terminal 100 executes the instructions to perform the operations described in the present disclosure.

[0045] The viewer 110 can display the results of image analysis stored in the image storage device 200. The viewer 110 can provide a worklist configured in a table format that lists and displays a list of images that the user needs to interpret along with key information. The viewer 110 can include a PACS (Picture Archiving and Communication System) viewer. Here, the viewer 110 is a program designed to display medical images and / or image analysis results stored in the image storage device 200 and can support image interpretation work related to the worklist, but is not necessarily limited to a viewer for interpretation work.

[0046] The image archiving device 200 can store and manage captured medical images. The image archiving device 200 can also store and manage analysis results for the medical images. The image archiving device 200 can include a PACS database. The image archiving device 200 can store data in a specified data format. For example, the image archiving device 200 can store medical images captured by a medical imaging device and analysis results for the medical images according to the Digital Imaging and Communications in Medicine (DICOM) standard, and can communicate with the user terminal 100 to provide data for image interpretation. The image archiving device 200 and the viewer 110 can be configured as a PACS system, where the image archiving device 200 can be a PACS server / DB and the viewer 110 can be a PACS viewer.

[0047] In this disclosure, the DICOM standard used for medical image storage will be used as an example for explanation, but the medical image standard does not need to be limited to DICOM.

[0048] The image storage device 200 can acquire medical image analysis results from the image analysis device 300. The medical image analysis results may include various medical predictions, including lesion information. The medical image analysis results can be provided to assist users in interpreting the images. The image analysis results can be provided in various formats, such as DICOM Secondary Capture (SC) and Grayscale Softcopy Presentation State (GSPS). SC generates an image (SC image) separate from the original medical image to display lesion information. It is provided separately from the original medical image and can be displayed in a PACS viewer. GSPS overlays lesion information on the original medical image. The overlaid lesion information can be turned on or off and displayed in a PACS viewer. In the present disclosure, SC will be mainly used as an example. The medical image analysis results can also be provided in a report in the form of text. For example, the report can be a DICOM Basic Text Structured Report (SR). However, the form of the medical image analysis results is not limited to this, and may include results in various DICOM formats.

[0049] The medical images stored in the image storage device 200 may include images acquired by medical imaging devices of various modalities. The medical images may be X-ray images, magnetic resonance imaging (MRI) images, ultrasound images, computed tomography (CT) images, digital mammography (MMG) images, digital breast tomosynthesis (DBT) images, etc. In this disclosure, chest X-ray images are described as an example of medical images, but the medical images are not limited thereto, and the present disclosure may be applied to any type of medical image.

[0050] The image analysis device 300 can analyze medical images using an artificial intelligence (AI) model and store the analysis results in the image storage device 200.

[0051] The video analysis device 300 may be equipped with an AI model specialized for each type of medical image, and may select an AI model according to the type of input image to perform analysis such as lesion detection suited to the input image. The AI ​​model is generated to perform medical inference from the input medical image, and the model structure, training data configuration, training method, medical inference target, etc. may be variously designed.

[0052] The video analysis device 300 can obtain analysis results of the medical image using multiple AI models trained to analyze medical images for different tasks, and generate a final analysis result for the medical image based on the analysis results. A report can be automatically generated based on the analysis results for the medical image. In this disclosure, it is assumed that the video analysis device 300 automatically generates the report, but the report generation can be performed by another device.

[0053] Although the number of AI models used by the video analysis device 300 is not necessarily limited to two, this disclosure assumes that AI model 1 (310) and AI model 2 (320) are used. In this case, whether to use AI model 1 (310) can be selectively determined. That is, the video analysis device 300 can analyze medical images using AI model 1 (310) and AI model 2 (320) depending on the settings, or can analyze medical images using only AI model 2 (320).

[0054] Referring to FIG. 2, AI model 1 (310) may be a model trained to comprehensively detect abnormal findings in medical images using more conservative criteria than AI model 2 (320). AI model 1 (310) can be trained to detect clinical abnormal findings as well as lesions such as nodules, pneumothorax, pleural effusion, consolidation, cardiomegaly, atelectasis, undulations, calcified degeneration, pulmonary fibrosis, mediastinal dilation, pulmonary tuberculosis, and fractures. AI model 1 (310) can be trained to detect more types of lesions as abnormal findings than AI model 2 (320). Here, clinical abnormal findings may include surgical scars, treatment scars, medical devices such as catheters, and other information that doctors must confirm for diagnosis and treatment.

[0055] AI model 1 (310) can be called a normal filtering model or a comprehensive analysis model in that it comprehensively detects abnormal findings in medical images and classifies not only lesions but also normal images that do not have any clinical abnormal findings that must be confirmed by a doctor.

[0056] The AI ​​model 1 (310) may output a score for an abnormal finding detected from a medical image, which may be referred to as a non-normal score. This is a term used to distinguish the score for an abnormal finding determined by the AI ​​model 1 (310) from an abnormality score, and the non-normal score may be replaced with other terms. The non-normal score may be a value related to the confidence level at which the AI ​​model 1 (310) believes that an abnormal finding exists in the medical image. The non-normal score may be defined as a score having a value within a specific range (e.g., 0 to 100) and / or as a probability value between 0 and 1. For example, if the non-normal score is defined as a probability value between 0 and 1, the normal score may be defined as the value obtained by subtracting the non-normal score from 1.

[0057] If the abnormality score predicted by the AI ​​model 1 (310) for the medical image is less than a threshold, the medical image is classified as normal; otherwise, the medical image is classified as abnormal. The threshold is a threshold for normal filtering, and the number of cases classified as normal may vary depending on the threshold. The threshold is variable. The threshold may be adjusted by the user, adjusted to an optimal value by the video analysis device 300, or the video analysis device 300 may suggest an optimal value to the user.

[0058] The AI ​​model 2 (320) may be a model trained to detect specific lesions in medical images. For example, lesions that can be detected in chest X-ray images may include nodules, pneumothorax, pleural effusion, consolidation, cardiomegaly, atelectasis, peritoneal edema, calcified degeneration, pulmonary fibrosis, mediastinal dilation, pulmonary tuberculosis, and fractures, as shown in Table 1.

[0059] [Table 1] AI model 2 (320) can output a score for a lesion detected in the medical image, which can be called a per-lesion score. The per-lesion score can be a value related to the confidence level with which AI model 2 (320) believes that the lesion exists in the medical image.

[0060] The medical image can be classified as normal or abnormal based on the lesion-specific score predicted by the AI ​​model 2 (320) for the medical image. For example, if at least one lesion among multiple detectable lesions has a lesion score equal to or greater than a threshold, the medical image can be classified as abnormal. The threshold can be set for each lesion. In addition, the lesion-specific threshold can be adjusted by the user, adjusted to an optimal value by the video analysis device 300, or the video analysis device 300 can recommend an optimal value to the user. For example, if the user considers the detection result of a specific lesion to be more important, the threshold for that specific lesion can be lowered.

[0061] If the lesion score predicted for a medical image is less than the threshold, an indication indicating that the presence of the lesion is unlikely, for example, "Low," may be output.

[0062] The abnormality score of the medical image can be determined based on the analysis result of the AI ​​model 2 (320). For example, the highest lesion score among the lesion scores can be determined as the abnormality score.

[0063] The image analysis device 300 can classify a medical image as a normal case if the medical image is determined to be completely normal by the AI ​​model 1 (310) and the AI ​​model 2 (320). That is, a medical image in which neither a lesion nor any clinical abnormality is found can be classified as a normal case. The image analyzer 300 can provide a score indicating a confidence level of whether the medical image is a normal case, i.e., a non-normal score or a normal score.

[0064] The image analysis device 300 can classify a medical image as an abnormal case if the medical image is determined to be abnormal by at least one of the AI ​​model 1 (310) and the AI ​​model 2 (320). Here, an abnormal case refers to a medical image that is clearly abnormal, and a medical image in which no lesion is detected but clinical abnormal findings such as surgical scars are detected may be classified as an abnormal case.

[0065] On the other hand, if AI model 1 (310) is trained to detect abnormal findings in medical images using more conservative criteria than AI model 2 (320), a medical image determined to be abnormal by AI model 2 (320) is unlikely to be determined to be normal by AI model 1 (310). Therefore, the image analysis device 300 can classify medical images determined to be normal by AI model 1 (310) and abnormal by AI model 2 (320) as exceptional cases. Exceptional cases can be handled in various ways; for example, the image analysis device 300 can be implemented to report the occurrence of an exceptional case to a designated device. The collected exceptional cases can be used to improve AI model 1 (310) and AI model 2 (320).

[0066] The function of determining whether a medical image is normal or not using the analysis results of AI Model 1 (310) can be activated by user settings. When AI Model 1 (310) is deactivated and medical images are analyzed using only AI Model 2 (320), the medical images can be set to be classified as normal or abnormal based on the analysis results of AI Model 2 (320). Alternatively, even if the analysis results of AI Model 2 (320) are normal, since no analysis was performed by AI Model 1 (310), the analysis results of AI Model 1 (310) can be considered abnormal, and the medical images can be set to be classified as abnormal.

[0067] The image analysis device 300 can generate a final analysis result for the medical image based on the analysis results of the AI ​​model 1 (310) and the AI ​​model 2 (320). The final analysis result can include analysis results including an abnormality score predicted by the AI ​​model 1 (310), a lesion score predicted by the AI ​​model 2 (320), an abnormality score, whether it is normal or not, lesion information, the presence or absence of normal cases classified based on the analysis results of the two models, abnormal scores for normal cases, medical indicators, whether the AI ​​model 1 (310) was used, etc.

[0068] The image analyzer 300 can provide the analysis results including lesion information in the medical image in formats such as DICOM Secondary Capture (SC), Grayscale Softcopy Presentation State (GSPS), and Structured Report (SR) based on the analysis results of the medical image. The analysis results for the medical image are stored in the image storage device 200 and can be displayed on the user terminal 100 linked to the image storage device 200. If the medical image is a DICOM image, the image metadata can be stored in a public tag and a private tag. Information about the medical image is recorded in the public tag according to the file structure defined by the DICOM standard. The private tag can be freely used by medical device companies when they want to add information not included in the public tag to the DICOM image, and the analysis results for the medical image can be recorded in the private tag. For example, the private tag may include the analysis results of AI model 1 (310) and AI model 2 (320), whether or not there are normal cases classified based on the analysis results of the two models, abnormal scores for normal cases, medical indicators, whether or not AI model 1 (310) was used, etc.

[0069] Analysis results for medical images can be provided to represent normal cases. For example, SC images or GSPS for normal cases can be provided in a different format than SC images or GSPS for abnormal cases so that users can quickly identify normal cases.

[0070] Medical images classified as normal cases can be filtered out of the worklist and saved in a separate folder. Users can view only the medical images classified as normal cases through that folder. Alternatively, normal cases can be displayed in the worklist without being excluded from the worklist, making it clear that they are normal cases. Users can identify and process normal cases from the worklist.

[0071] The viewer 110 executed in the user terminal 100 can provide analysis results for medical images. The viewer 110 can display various information included in the analysis results, and therefore, the analysis results can be saved in a DICOM format, which is a data format that can be displayed by the viewer 110. The analysis results for medical images can be provided in SC, GSPS, SR, etc.

[0072] 3, an SC image 400A for a normal case may be configured with an image area 410A displaying a medical image and an information area 420A displaying the analysis results of the medical image. The information displayed in the information area 420A may be defined in various ways. The information area 420A may display a non-normal score or a normal score indicating the confidence level for the normal case, or may display a score for a specified medical index and / or lesion or a level compared to a threshold (e.g., "Low" indicating a level lower than the threshold). In the case of a normal case, the information area 420A may be omitted.

[0073] The SC image 400A for a normal case may further display a graphical indicator 430A indicating that the medical image is a normal case. The position and display method of the graphical indicator 430A may be defined in various ways. For example, the graphical indicator may be displayed as an "N" icon. "N" may mean "No visible abnormality" or "Normal."

[0074] For example, if a chest X-ray image is determined to be normal by both AI model 1 (310) and AI model 2 (320), the chest X-ray image can be classified as a normal case. The SC image provided for a normal case can display designated information (e.g., Abnormality Score: Low, TB analysis score: Low) in information area 420A and a graphical indicator 430A indicating that it is a normal case. The abnormality score of the medical image may be displayed in information area 420A, and since it is a normal case, it can be displayed as "Low," indicating a level lower than the threshold. Meanwhile, the lesion score for pulmonary tuberculosis can be displayed separately from the abnormality score and can be displayed as a pulmonary tuberculosis analysis score (TB analysis score).

[0075] When providing the results of medical video analysis in GSPS format, information indicating a normal case (e.g., "N") can be overlaid on the medical video.

[0076] Through the graphical indicators that indicate normal cases, users can intuitively recognize that all medical images have been judged normal by the AI ​​model.

[0077] 4 and 5, the SC images 400B and 400C for the abnormal cases may be configured with image areas 410B and 410C in which medical images are displayed and information areas 420B and 420C in which analysis results of the medical images are displayed. In this case, the SC images 400B and 400C for the abnormal cases do not provide a graphical indicator indicating that the medical images are normal cases. Instead, a graphical indicator indicating that the medical images are abnormal cases may be displayed.

[0078] If a medical image is judged as abnormal by both AI model 1 (310) and AI model 2 (320), or if it is judged as abnormal by AI model 1 (310) and normal by AI model 2 (320), the medical image can be classified as an abnormal case.

[0079] 4, when a medical image is determined to be abnormal by both AI model 1 (310) and AI model 2 (320), SC image 400B provided for the abnormal case provides a medical image in which lesion information is visually displayed in image area 410B, and the lesion information may include the location of the lesion area and the lesion score. For example, image area 410B may display the lesion name and lesion score, such as Fx92, Csn22, Atl, and Csn57, on top of the medical image, and the lesion area may be displayed as an outline, heat map, etc.

[0080] The information area 420B may display specified information (e.g., Abnormality Score: 92, TB analysis score: Low) among the analysis results of the medical image. The information area 420B may display the abnormality score of the medical image, a level compared to a lesion-specific score or a lesion-specific threshold (e.g., "Low" indicating a level lower than the threshold). The abnormality score of the medical image may be the highest lesion score among the lesion-specific scores. If the lesion score of a specific lesion is equal to or higher than the threshold, the lesion score may be displayed in the information area. If the lesion score of a specific lesion is lower than the threshold, "Low" indicating a level lower than the threshold may be displayed.

[0081] Referring to Figure 5, when a medical image is judged as abnormal by AI model 1 (310) and as normal by AI model 2 (320), the SC image 400C provided for the abnormal case is judged as normal by AI model 2 (320), so the lesion information detected by AI model 2 (320) may not be displayed in the image area 410C, or the lesion information and clinical abnormal findings detected by AI model 1 (310) may be displayed in a specified manner.

[0082] Information area 420C may display specified information, for example, an indication that a specified lesion score is below a threshold level, and / or an abnormality score or level of abnormality score (e.g., "Low", "High").

[0083] Meanwhile, the use of AI model 1 (310) can be selectively determined, and the SC image can display only the analysis results by AI model 2 (320). If only the analysis results by AI model 2 (320) exist, the medical image can be classified as an abnormal case because it is not a normal case where both models have determined that the medical image is normal. If the analysis results of the medical image by AI model 2 (320) are abnormal, the SC image for the medical image can be provided as shown in Figure 4. If the analysis results of the medical image by AI model 2 (320) are normal, the SC image for the medical image can be provided as shown in Figure 5.

[0084] In this way, the image analysis device 300 can improve the accuracy and reliability of the analysis results by analyzing medical images through AI model 1 (310) and AI model 2 (320). The image analysis device 300 classifies normal medical images with high reliability among medical images and provides graphical indicators that allow users to recognize normal cases, thereby reducing the interpretation time and workload and improving interpretation efficiency. As a result, the memory and computing resources of the medical imaging system 1 for managing medical images waiting for interpretation can be reduced. In addition, because the image analysis device 300 classifies normal medical images with high reliability among medical images, medical institutions that use the system can reduce interpretation costs.

[0085] The image analysis device 300 automatically generates interpretation text based on the analysis results of medical images by AI model 1 (310) and AI model 2 (320), thereby reducing the interpretation time and workload and improving interpretation efficiency. As a result, the memory resources and computing resources of the medical image system 1 for managing medical images waiting for interpretation can be reduced.

[0086] FIG. 6 is a diagram illustrating a method for setting a threshold for normality filtering according to an embodiment.

[0087] 6, if the non-normal score predicted by AI model 1 (310) for a medical image is less than a threshold, the medical image is classified as normal, and if not, the medical image can be classified as abnormal. Although such normal filtering threshold can be used in the same way at all medical institutions to classify medical images, the number of images determined as normal by AI model 1 (310) varies depending on the normal filtering threshold, and therefore the number of images classified as normal cases varies, which may affect the interpretation workload and interpretation efficiency.

[0088] Also, referring to Table 2, patient characteristics differ from medical institution to medical institution, and the ratio of normal cases, borderline cases, and abnormal cases differs. Therefore, the normality filtering threshold can be adjusted according to the case distribution of such a medical institution. The normality filtering threshold can be adjusted by changing the user's configuration value, adjusted to an optimal value by the video analysis device 300, or the video analysis device 300 can recommend an optimal value to the user.

[0089] [Table 2] When a medical institution is configured to use AI model 1 (310), the normal filtering threshold for determining a normal image through AI model 1 (310) can be set as a basic value (default). Thereafter, the image analysis device 300 statistically processes the result data of the medical image analyzed using AI model 1 (310) and AI model 2 (320) at each medical institution, and can change the normal filtering threshold and / or suggest an appropriate value based on the result data. Here, the image analysis device 300 is not limited to suggesting an optimal single value, but can suggest an adjustable range of normal filtering thresholds and allow the user to select a threshold within the range.

[0090] The image analysis device 300 may provide the user with result data for the medical image in the form of a graph and / or a dashboard, and may provide the currently set normality filtering threshold and / or a recommended normality filtering threshold along with the result data. The result data for the medical image may include the number, ratio, and case distribution chart of normal, borderline, and abnormal cases. The case distribution chart may be expressed as the number of cases relative to the abnormality score, and the definitions of normal, borderline, and abnormal cases may vary. For example, medical images determined to be normal according to the normality filtering threshold set in AI model 1 (310) may be viewed as normal cases, or medical images ultimately determined to be normal by a medical interpreter may be viewed as normal cases.

[0091] The video analysis device 300 can analyze medical images of a medical institution using the AI ​​model 1 (310). Also, it can propose a normal filtering threshold for the medical institution based on data such as normal case information (number of normal cases, ratio of normal cases, distribution map of normal cases, etc.) for medical images of the medical institution analyzed using the video analysis device 300 for a certain period of time, abnormal case information in which clinically relevant lesions are detected (number of abnormal cases, ratio of abnormal cases, distribution map of abnormal cases, etc.), and distribution map of specialists' findings for images taken at the medical institution.

[0092] The video analysis device 300 may propose a normal filtering threshold for each medical institution based on various information. For example, the video analysis device 300 may propose a normal filtering threshold based on a distribution map of normal cases, borderline cases, and abnormal cases of the medical institution. The video analysis device 300 may propose a normal filtering threshold based on the type of medical institution.

[0093] For example, when looking at a case distribution chart for a medical institution 1 such as a health checkup center, there are more normal cases than abnormal cases, and the normal cases are distributed unevenly with low abnormal scores. Furthermore, since borderline cases must be recommended for additional examinations in accordance with the health checkup center's objectives, the image analysis device 300 can recommend a normality filtering threshold, which corresponds to an abnormality score, lower than the basic threshold. This allows the health checkup center to achieve its objectives by more conservatively determining whether a case is normal or not using the AI ​​model 1 (310). Since the majority of cases are normal, lowering the normality filtering threshold does not result in a sudden increase in abnormal cases. Furthermore, by setting a low abnormality score as the threshold, it is possible to classify multiple medical images that are definitely normal.

[0094] When looking at the distribution of cases at medical institution 2, such as a tertiary hospital, it is found that there are many abnormal cases and most of the borderline cases are determined to be lesions that are not clinically significant, so the image analysis device 300 can propose a normal filtering threshold, which corresponds to an abnormal score, that is higher than the basic threshold. In the case of medical images of patients visiting a tertiary hospital, the probability of detecting abnormal findings such as lesions is high and the abnormal score is relatively high. Therefore, even if the normal filtering threshold of AI model 1 (310) is increased, the number of normal cases does not increase sharply, and by setting a high abnormal score as the threshold, it is possible to classify medical images in which clinically significant lesions are detected.

[0095] In this way, even if medical images are analyzed through AI model 1 (310), medical images from medical institution 1 can be classified as abnormal even if the probability of abnormal findings is lower than the base value, and medical images from medical institution 2 can be classified as abnormal if the probability of abnormal findings is higher than the base value. This allows for optimizing interpretation work for each medical institution, as the weighting of normal images among all images varies depending on the country, hospital size, and clinical environment.

[0096] Meanwhile, the user terminal 100 may provide an interface screen that allows the user to adjust the normal filtering threshold for AI model 1 (310) and the lesion-specific threshold for AI model 2 (320). The interface screen may display recommended thresholds. The user may adjust at least one of the normal filtering threshold for AI model 1 (310) and the lesion-specific threshold for AI model 2 (320) through the interface screen, and the threshold adjusted by the user may be set in the image analysis device 300. The user terminal 100 may also provide the user with result data of the analysis of medical images of each medical institution by the image analysis device 300 over a certain period in the form of a graph and / or dashboard. The user may understand the characteristics of the medical institution based on the result data and set a definition of normal for each lesion. For example, the user may set a lesion score of 30 or less as normal for calcification, a lesion score of 25 or less as normal for pulmonary fibrosis, and a medical device among clinical abnormal findings as normal.

[0097] FIG. 7 is an illustration of a worklist generated according to one embodiment.

[0098] 7, the viewer 110 executed on the user terminal 100 can provide a worklist 500 in conjunction with the image storage device 200. The worklist 500 displays a list of images that the user must read together with key information in a table format. The worklist does not necessarily have to be included in the viewer 110 and can be installed as a separate program. The information displayed in the worklist can be provided by the viewer 110 or the image storage device 200, but for convenience of explanation, it can be described as being provided by the viewer 110.

[0099] The viewer 110, in conjunction with the image storage device 200, can display analysis results of medical images analyzed by the image analysis device 300 in designated columns 510 and 520 of the worklist 500. The analysis results displayed in the worklist can be configured in various ways. For example, the abnormality score of each image can be listed in column 510, and the presence or absence of normal cases can be listed in a "No Visible Abnormality" column 520. For example, the viewer 110 can classify data regarding the presence or absence of normal cases included in the private tag of the medical image into color chips or flags and display them in the worklist. The viewer 110 can distinguish and display images included in the worklist according to the priority determined by the comparison analysis results. In addition, the viewer 110 can sort and display the images in the worklist according to the priority determined by the comparison analysis results.

[0100] If AI model 1 (310) and AI model 2 (320) determine that a particular medical image is completely normal, the worklist 500 can display (e.g., flag or highlight) the specific medical image in the "No Visible Abnormality" column 520 to indicate that it is a normal case, and can provide a function for sorting or extracting normal cases for interpretation. This allows the user (radiography specialist) to identify normal cases and determine the order of work, and can set priorities to read abnormal cases before normal cases. In addition, the user can devote more time to interpretation of abnormal cases than normal cases. Since the SC image or GSPS of a normal case includes an indication that it is a normal case, the user can shorten the time it takes to interpret normal cases.

[0101] The worklist 500 can display the "No Visible Abnormality" column 520 when the function for determining whether a medical image is normal using the analysis results of AI model 1 (310) is activated, and can hide the "No Visible Abnormality" column 520 when the function is deactivated.

[0102] Meanwhile, the worklist 500 may provide a function that allows normal cases, which are judged to be completely normal by the AI ​​model 1 (310) and the AI ​​model 2 (320), to be viewed separately by not displaying them or by saving them in a separate folder. Furthermore, normal cases may not be assigned to the worklist for interpretation, or if an interpretation statement is automatically generated and saved, an indicator indicating that an interpretation statement for the normal case image has been automatically generated may be displayed in the worklist. The user can recognize through the indicator that an interpretation statement requiring user confirmation has been generated. That is, the medical imaging system 1 may provide an interpretation procedure that does not require the user to interpret the medical images of normal cases. An interpretation statement for a normal case may be automatically generated based on the analysis results of the medical images and saved in the image storage device 200. The video analysis device 300 may generate an interpretation statement for a normal case and save it in a designated folder in the image storage device 200. The image analysis device 300 can generate interpretations based on the analysis results of medical images other than normal cases, thereby reducing the time required for users to create interpretations. The automatically generated interpretations can be saved after being corrected and final checked by a designated user. Meanwhile, the image storage device 200 or a separate device can generate interpretations based on the analysis results of medical images.

[0103] In addition, the medical imaging system 1 can assign interpretation tasks for normal cases to less skilled users and / or to remote interpretation companies, thereby efficiently managing interpretation tasks within the hospital, thereby improving the quality of medical services provided to patients.

[0104] FIG. 8 is an example of a deciphered sentence generated according to one embodiment.

[0105] 8, the image analysis device 300 or a designated device can generate an interpretation based on the analysis results of the medical image. For example, an interpretation 600 can be generated for a normal case where all the images are determined to be normal by AI model 1 (310) and AI model 2 (320).

[0106] Interpretation 600 can be structured, for example, with a name region 610 , a patient and image information region 620 , and an analysis region 630 .

[0107] The name field 610 contains the name of the interpretation (e.g., Chest CAD Report), and may contain an explanation for the interpretation, such as explanation 1 (This preliminary report is created by Lunit.), indicating that the interpretation was automatically generated by AI rather than by a human.

[0108] The patient information and image information area 620 can include items such as a medical image identifier 2 (e.g., StudyInstanceUID), patient age 3 (Patient Age), patient gender 4 (Sex), and medical image information 5 (Technique). The user can set these items on the setting page. The values ​​entered in each item of the patient information and image information area 620 can be extracted from the corresponding DICOM tag of the medical image.

[0109] The analysis area 630 can include items such as findings 6, additional comments 7, and overall impressions 8. As with the items included in the patient information and image information area 620, the items included in the analysis area 630 can be configured by the user on the settings page. The wording to be written in findings 6 (for example, for chest X-ray images, Lines and tubes: [none]) can be changed. present], Lungs and pleural space: [No focal consolidation, pleural effusion or pneumothorax, etc.] can also be set by the user on the setting page. In normal cases, all findings may be described as normal. For the comments written in Additional Comments 7, the user can set the wording on the setting page, and if the user does not write an additional comment, the Additional Comments 7 area may not be displayed. In normal cases, Overall Comments 8 may display an explanation that the medical image is normal (e.g., Normal chest radiograph).

[0110] In this way, interpretation statements for normal cases can be automatically generated in a format preset by the user. Automatic generation of interpretation statements for normal cases can reduce the time required by the user (radiography specialist) to interpret normal images and create interpretation statements. Meanwhile, in order to automate the generation of interpretation statements for normal images, it is necessary to accurately classify normal images. The video analysis device 300 can increase the reliability of the automatically generated interpretation statements by classifying medical images that are determined to be completely normal by AI model 1 (310) and AI model 2 (320) as normal cases.

[0111] FIG. 9 is a flowchart of a video analysis method according to an embodiment.

[0112] Referring to FIG. 9, the video analysis device 300 obtains analysis results for medical images using AI model 1 (310) and AI model 2 (320), which are trained to analyze medical images for different tasks (S110). AI model 1 (310) is a normality filtering model trained to comprehensively detect abnormal findings in medical images using more conservative criteria than AI model 2 (320), and can detect lesions and clinical abnormal findings as abnormal findings. AI model 2 (320) may be a model trained to detect specific lesions in medical images. The video analysis device 300 can obtain analysis results including abnormality scores predicted by AI model 1 (310), and analysis results including lesion-specific scores, abnormality scores, normality, and lesion information predicted by AI model 2 (320). Meanwhile, the video analysis device 300 can analyze medical images using AI model 1 (310) and AI model 2 (320) depending on the settings, or can analyze medical images using only AI model 2 (320).

[0113] The video analysis device 300 classifies medical images that are determined to be normal by both AI model 1 (310) and AI model 2 (320) based on the analysis results of the medical images as normal cases (S120). If the abnormality score obtained as the analysis result of AI model 1 (310) is less than the normality filtering threshold, the video analysis device 300 may determine that the medical image has been determined to be normal by AI model 1 (310). In this case, the normality filtering threshold for determining whether the medical image is normal or abnormal based on the abnormality score may be variable. The video analysis device 300 may determine whether the medical image is normal or abnormal based on the lesion-specific score obtained as the analysis result of AI model 2 (320) or an abnormality score determined from the lesion-specific score. The lesion-specific threshold for determining whether the medical image is normal or abnormal based on the lesion-specific score may be variable. The video analysis device 300 may change the normality filtering threshold, lesion-specific threshold, etc. based on user input. The video analysis device 300 may suggest a new normality filtering threshold to the user.

[0114] The image analysis device 300 provides the analysis results of the medical image by the AI ​​model 1 (310) and the AI ​​model 2 (320) and the final analysis results including the presence or absence of normal cases in the medical image to a designated device (S130). The final analysis results for the medical image are generated in DICOM format and can be provided to the image storage device 200. The final analysis results for the medical image are stored in the image storage device 200 of, for example, a PACS server / DB, and can be displayed through the viewer 110 on the user terminal 100 linked to the image storage device 200.

[0115] The image analysis device 300 generates an interpretation for the medical image classified as a normal case and provides the interpretation to a designated device (S140). The image analysis device 300 can generate an interpretation structured to include patient information and image information extracted from the DICOM tag of the medical image, normal findings preset for the normal case, and an overall opinion that the medical image is normal.

[0116] Medical images may be handled differently in the medical imaging system 1 depending on whether or not they contain normal cases. SC images or GSPS images of normal cases may include a graphical indicator indicating that they are normal cases. When medical images of normal cases are assigned to a worklist, a display (flag display) indicating that they are normal cases may be added. Medical images of normal cases may not be assigned to a worklist for interpretation or may not be displayed in the worklist. Medical images of normal cases may be stored in a separate folder so that users can review only the cases they are interested in. When interpretation statements for normal medical images are automatically generated and stored, an indicator indicating that an interpretation statement for the medical image has been automatically generated may be displayed in the worklist. The indicator allows users to know that an interpretation statement requiring user confirmation has been automatically generated.

[0117] FIG. 10 is a flowchart of a method for setting a threshold for normality filtering according to one embodiment.

[0118] 10, the video analysis device 300 obtains an abnormality score for a medical institution's medical image using AI model 1 (310) and determines that a medical image whose abnormality score is below a normality filtering threshold is normal (S210). AI model 1 (310) is a normality filtering model trained to comprehensively detect abnormal findings in a medical image using conservative criteria and can detect lesions and clinical abnormal findings as abnormal findings. In addition, the video analysis device 300 analyzes the medical image using AI model 2 (320), which is trained to detect specific lesions in the medical image, and classifies medical images that are determined to be normal by both AI model 1 (310) and AI model 2 (320) as normal cases.

[0119] The video analysis device 300 collects normal case information and abnormal case information for medical images from a medical institution and generates a distribution chart of normal / abnormal cases for the medical institution relative to abnormal scores (S220). The normal case information includes the number of normal cases, the ratio of normal cases, and a distribution chart of normal cases, while the abnormal case information is information on cases in which clinically significant lesions are detected and may include the number of abnormal cases, the ratio of abnormal cases, and a distribution chart of abnormal cases. The medical images of normal cases and abnormal cases are mapped with abnormal scores determined by AI model 1 (310), and the video analysis device 300 can generate a graph showing the number of normal / abnormal cases for each abnormal score. The video analysis device 300 can provide the generated graph.

[0120] The image analysis device 300 determines a normality filtering threshold for optimizing normality filtering of the medical image by the AI ​​model 1 (310) based on at least one of the medical institution type and the distribution map of normal cases or abnormal cases for the medical institution relative to the abnormal score, and proposes a new normality filtering threshold (S230). The logic for optimizing normality filtering can be designed in various ways depending on the optimization conditions. The image analysis device 300 can provide the distribution map of medical institution cases relative to the abnormal score and / or the proposed new normality filtering threshold or threshold range to the user through the viewer 110. The user can view the distribution map of medical institution cases and / or the proposed new normality filtering threshold or threshold range in a setting window provided in the form of a user interface, and set the new normality filtering threshold.

[0121] When the new normality filtering threshold is set, the image analysis device 300 determines whether the medical image analyzed by the AI ​​model 1 (310) is normal or abnormal using the new normality filtering threshold (S240).

[0122] FIG. 11 is a flowchart of a method for providing analysis results according to one embodiment.

[0123] Referring to FIG. 11, the viewer 110 displays a worklist including an image list for image interpretation work in conjunction with the image storage device 200, which stores analysis results for medical images (S310). The viewer 110 displays the analysis results for each image analyzed by the image analyzer 300 in a designated column of the worklist, and the analysis results may include an abnormality score and the presence or absence of normal cases. The worklist may provide a function to not display or hide medical images of normal cases that are determined to be completely normal by AI model 1 (310) and AI model 2 (320). Normal cases may not be assigned to the worklist for interpretation. If an interpretation statement for a normal case is automatically generated and saved, a distinctive display may be provided on the worklist. If an interpretation statement for a normal case is automatically generated and saved, an indicator indicating that an interpretation statement requiring user confirmation has been generated may be displayed on the worklist.

[0124] When a medical image is selected from the worklist, the viewer 110 displays a secondary capture (SC) image showing the analysis result of the medical image, and displays the SC image for a normal case so as to be distinguished from the SC image for an abnormal case (S320). The viewer 110 can display captured medical images and SC images in DICOM format. SC images for normal cases can be provided in a different format from SC images for abnormal cases so that a user can quickly identify the normal cases. For example, the SC images for normal cases can display a graphical indicator indicating that the medical images are normal cases.

[0125] The viewer 110 provides automatically generated interpretations for medical images classified as normal cases (S330). That is, the user does not need to manually create interpretations for medical images classified as normal cases, but can check the automatically generated interpretations using pre-set normal finding expressions for normal cases and correct them as necessary.

[0126] The viewer 110 may be implemented as a computer program stored in a computer-readable storage medium and may include instructions for execution by a processor. The computer program may include instructions for causing the processor to, in conjunction with the image storage device 200, display a worklist including a list of images for image interpretation work, and, when a medical image is selected from the worklist, display the analysis results of the medical image.

[0127] The terminal or device 100, 200, or 300 constituting the medical imaging system 1 of the present disclosure may include one or more processors, a memory for loading a computer program executed by the processor, a storage device for storing the computer program and various data, and a communication interface. The terminal or device 100, 200, or 300 may further include various other components. The processor may be any of various types of processors that process instructions included in a computer program, such as at least one of a central processing unit (CPU), a microprocessor unit (MPU), a microcontroller unit (MCU), a graphic processing unit (GPU), or any other type of processor well known in the technical field of the present disclosure. The memory stores various data, instructions, and / or information. The memory may be configured to store instructions written to perform the operations of the present disclosure so that the instructions can be processed by the processor. The memory may be, for example, a read-only memory (ROM), a random access memory (RAM), etc. The storage device may non-temporarily store the computer program and various data. The storage device may be configured to include a non-volatile memory such as a read only memory (ROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a flash memory, a hard disk, a removable disk, or any other form of computer-readable recording medium well known in the art to which the present disclosure pertains. The communication interface may be a wired / wireless communication module supporting wired / wireless communication.The computer program includes instructions to be executed by a processor and is stored in a non-transitory computer-readable storage medium, and the instructions cause the processor to perform the operations of the present disclosure. After acquiring a target image of a patient, the image analyzer 300 can analyze the target image to obtain an analysis result including lesion information.

[0128] The embodiments of the present disclosure described above may be implemented not only through devices and methods, but also through a program that realizes functions corresponding to the configurations of the embodiments of the present disclosure or a recording medium on which the program is recorded.

[0129] Although the embodiments of the present disclosure have been described in detail above, the scope of the present disclosure is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concept of the present disclosure defined in the appended claims also fall within the scope of the present disclosure.

Claims

1. A computer program stored on a computer-readable storage medium, comprising: instructions that, when executed by a processor, cause a viewer to be provided; The viewer is a computer program that works in conjunction with an image storage device that stores analysis results of medical images of a medical institution, displays a worklist including an image list for image interpretation work, and displays medical images classified as normal cases from the worklist in a distinguishable manner based on whether or not there are normal cases included in the analysis results of the medical images.

2. The viewer When a specific medical image classified as the normal case is selected from the worklist, a secondary capture (SC) image or a grayscale softcopy presentation state (GSPS) image showing an analysis result of the specific medical image is displayed; The computer program of claim 1 , wherein the SC image or the GPSS image includes a graphical indicator indicating that the particular medical image is the normal case.

3. The viewer providing an interpretation generated for the medical image classified as a normal case; The computer program of claim 1 , wherein the interpretation sentence for the medical image classified as a normal case is automatically generated using a normal finding sentence preset for the normal case.

4. The computer program of claim 3 , wherein the readable text is generated in a format set by a user on a setting page.

5. The viewer The computer program product of claim 3 , further comprising displaying an indicator in the worklist indicating the generation of the interpreted sentence.

6. The computer program of claim 1 , wherein the medical images classified as normal cases are stored in a separate folder other than the worklist so that the medical images can be viewed through the separate folder.

7. A computer program stored on a computer-readable storage medium, comprising: The method includes instructions for, when executed by a processor, determining an adjustable range of a normality filtering threshold, and providing a user interface including the adjustable range of the normality filtering threshold; The normal filtering threshold is This is a value used to classify normal cases among medical images through analysis of the medical images of a medical institution using an artificial intelligence model, A computer program that modifies the current normal filtering threshold by a value entered in the adjustable range.

8. The user interface includes: The computer program of claim 7 , wherein the result data for the medical image of the medical institution is displayed in at least one form of a graph and a dashboard.

9. The adjustable range of the normal filtering threshold is: The computer program according to claim 7 , wherein the determination is based on at least one of the type of the medical institution, a distribution map of normal cases of the medical institution, and a distribution map of abnormal cases of the medical institution.

10. The adjustable range of the normal filtering threshold is: The computer program according to claim 7 , wherein the determination is based on a distribution map of findings of specialists on medical images from the medical institution.

11. 1. A method for providing a viewer by a computing device, comprising: Displaying a worklist including an image list for image interpretation work in conjunction with an image storage device that stores analysis results of medical images of a medical institution; and displaying medical images classified as normal cases in the worklist in a distinctive manner based on whether or not there are normal cases included in the analysis results of the medical images. A method comprising:

12. and when the specific medical image classified as the normal case is selected from the worklist, displaying a secondary capture (SC) image or a grayscale softcopy presentation state (GSPS) image showing an analysis result of the specific medical image; The method of claim 11 , wherein the SC image or the GSPS image includes a graphical indicator indicating that the particular medical image is the normal case.

13. providing a generated interpretation for the medical image classified as a normal case; The method of claim 11 , wherein the interpretation of the medical image classified as a normal case is automatically generated using a normal finding expression preset for the normal case.

14. The method of claim 13 , wherein the readable text is generated in a format set by a user on a settings page.

15. The method of claim 13 , further comprising displaying an indicator in the worklist indicating the generation of the interpretation.

16. The method of claim 11 , wherein the medical images classified as normal cases are stored in a separate folder other than the worklist so that the medical images can be viewed through the separate folder.

17. 1. A method of providing a user interface by a computing device, comprising: determining an adjustable range for the normality filtering threshold; and The method further includes providing a user interface that displays an adjustable range of the normal filtering threshold; The normal filtering threshold is: A value used to classify normal cases among medical images through analysis of the medical images of a medical institution using an artificial intelligence model, The method wherein the current normal filtering threshold is changed by the value entered in the adjustable range.

18. The user interface includes: The method of claim 17, wherein the result data for the medical images of the medical institution is displayed in the form of at least one of a graph and a dashboard.

19. The determining step comprises: The method of claim 17 , wherein the adjustable range is determined based on at least one of a type of the medical institution, a distribution map of normal cases of the medical institution, and a distribution map of abnormal cases of the medical institution.

20. The determining step comprises: The method according to claim 17 , wherein the adjustable range is determined based on a distribution map of findings of specialists on medical images of the medical institution.

Images