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WO2020255171A1 - Système et procédé de diagnostic à distance d'une pluralité d'images - Google Patents

Système et procédé de diagnostic à distance d'une pluralité d'images Download PDF

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Publication number
WO2020255171A1
WO2020255171A1 PCT/IN2020/050542 IN2020050542W WO2020255171A1 WO 2020255171 A1 WO2020255171 A1 WO 2020255171A1 IN 2020050542 W IN2020050542 W IN 2020050542W WO 2020255171 A1 WO2020255171 A1 WO 2020255171A1
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WO
WIPO (PCT)
Prior art keywords
image
input
images
user interface
module
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IN2020/050542
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English (en)
Inventor
Mrinal HALOI
Raja RAJA LAKSHMI
Rajarajeshwari KODHANDAPANI
Pradeep WALIA
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Artificial Learning Systems India Pvt Ltd
Original Assignee
Artificial Learning Systems India Pvt Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Artificial Learning Systems India Pvt Ltd filed Critical Artificial Learning Systems India Pvt Ltd
Publication of WO2020255171A1 publication Critical patent/WO2020255171A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Definitions

  • the present invention relates to a system and method for remote diagnostic analysis of Image Modality. Specifically, the present invention relates to the prediction of diseases by a computer implemented system based on the Artificial Intelligence based Machine learning algorithm.
  • Another object of the invention is to provide a method for diagnosis of the plurality images from a remote location based on the machine learning / Artificial Learning algorithm algorithm of the system.
  • a computer implemented system to detect a plurality of diseases from captured plurality images comprises of at least one processor; and one or more storage devices configured to store instructions configured for execution by the at least one processor in order to cause the system to receive a plurality of image of a patient; identify a plurality of indicators throughout the plurality of image using a convolutional network; the non-transitory computer readable storage medium configured to store a plurality of image analysis application.
  • the plurality of image analysis application comprises a graphical user interface comprising a plurality of interactive elements configured to enable capture and analysis of the plurality of image via a user device; a reception means, adapted to receive an input from an image capturing device based on a plurality of parameters of the image capturing device, wherein the input is the plurality of image of the patient.
  • the system further comprises of an interactive plurality of image rendering means, adapted to dynamically render the input, wherein the dynamically rendered input is configurable accessible on the graphical user interface via the user device using the interactive elements; and the plurality of image capture means, adapted to capture the plurality of image based on the dynamically rendered input.
  • the system further comprises of an administrator module, wherein created for an admin to provide access to a plurality of client users like an institute module, created to give access to plurality of Institutes of their login by admin based on the requirement; an uploader module, created to give access to plurality of uploaders to upload bulk images of their login by admin based on their requirement; an annotator module, created to give access to plurality of annotators of their login to annotate the individual images by admin based on the requirement; and a prediction module, created as a reporting tool that displays a disease based on the uploaded images using a convolutional network.
  • an administrator module wherein created for an admin to provide access to a plurality of client users like an institute module, created to give access to plurality of Institutes of their login by admin based on the requirement
  • an uploader module created to give access to plurality of uploaders to upload bulk images of their login by admin based on their requirement
  • an annotator module created to give access to plurality of annotators of their login to annotate the individual images by
  • the system detects at least one disease selected from such as diabetic retinopathy, glaucoma, TB, pneumonia, breast cancer and other diseases related to pathological failure.
  • the system detects at least one of a lesion, a venous beading, a venous loop, an intra retinal microvascular abnormality, an intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/prerenal hemorrhage, neovascularization, a venous beading, a venous loop, a retinal microvascular abnormality, a lesion, an intra retinal microvascular abnormality, an intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/preretinal hemorrhage, neovascularization, pathological
  • a computer implemented method to detect a plurality of diseases from captured plurality of images comprises a step of providing, a plurality of image for analysis accessible by a user device via a graphical user interface of an image analysis module;
  • the method comprises another step of providing, a plurality of interactive elements on the graphical user interface of the plurality of image analysis module, the interactive elements configured to enable capture and analyze the plurality of images.
  • the computer implemented method comprises a step of receiving, an input from an image capturing device based on a plurality of parameters of the image capturing device by the plurality of image analysis application via the graphical user interface, wherein the input is the plurality of image of the patient displayed in a live mode.
  • the computer implemented method comprises a step of dynamically rendering, the input by the plurality of image analysis application via the graphical user interface, wherein the dynamically rendered input is configurable accessible on the graphical user interface via the user device using the interactive elements.
  • the computer implemented method comprises a step of capturing, the plurality of image based on the dynamically rendered input by the plurality of image analysis application via the graphical user interface; a step of identifying, a plurality of indicators throughout the plurality of image using a convolutional network; and a step of detecting, a presence or absence of a retinal disease, and other diseases such as TB, Pneumonia, Brest cancer and other pathological diseases based the identified indicators using the convolutional network; and a step of classifying, a severity of the retinal disease based on the presence or absence of the retinal disease using the convolutional network.
  • the computer implemented method deeds as an indicator is one of an abnormality, a retinal feature, and other diseases such as TB, Pneumonia, Brest cancer and other pathological diseases or the like.
  • the computer implemented method identifies an abnormality is one of a lesion, a venous beading, a venous loop, an intra retinal microvascular abnormality, an intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/preretinal hemorrhage, neovascularization, pathological failure, TB (Tuberculosis), Pneumonia, etc or the like.
  • FIG. 1 illustrates a schematic layout of the institute login according to an embodiment of the present invention
  • FIG. 2 illustrates the screen layout of the members tab according to an embodiment of the present invention
  • FIG. 3 illustrates the screen layout of the dashboard tab according to an embodiment of the present invention
  • FIG. 4 illustrates the screen layout of the send invite tab according to an embodiment of the present invention
  • FIG. 5 illustrates a report template according to an embodiment of the present invention
  • FIG. 6 illustrates a schematic layout of the doctor login according to an embodiment of the present invention
  • FIG. 7 illustrates the screen layout of the patient visits tab according to an embodiment of the present invention
  • FIG. 8 illustrates the screen layout of the patient view tab according to an embodiment of the present invention
  • FIG. 9 illustrates a schematic layout of the uploader according to an embodiment of the present invention.
  • FIG. 10 illustrates the screen layout of the upload tab according to an embodiment of the present invention
  • FIG. 11 illustrates a schematic layout of the annotator login according to an embodiment of the present invention
  • FIG. 12 illustrates a screen layout of the set bucket tab according to an embodiment of the present invention
  • FIG. 13 illustrates a screen layout of the History tab according to an embodiment of the present invention
  • FIG. 14 illustrates a schematic layout of the predictor login according to an embodiment of the present invention
  • FIG. 13 illustrates a screen layout of the Predict tab according to an embodiment of the present invention.
  • a computer implemented system to detect a plurality of diseases from captured plethora of images comprises of at least one processor; and one or more storage devices configured to store instructions configured for execution by the at least one processor in order to cause the system to receive a plurality of image of a patient; identify a plurality of indicators throughout the plurality of image using a convolutional network; the non-transitory computer readable storage medium configured to store a plurality of image analysis application.
  • the plurality of image analysis application comprises a graphical user interface comprising a plurality of interactive elements configured to enable capture and analysis of the plurality of image via a user device; a reception means, adapted to receive an input from an image capturing device based on a plurality of parameters of the image capturing device, wherein the input is the plurality of image of the patient.
  • the system further comprises of an interactive plurality of image rendering means, adapted to dynamically render the input, wherein the dynamically rendered input is configurable accessible on the graphical user interface via the user device using the interactive elements; and the plurality of image capture means, adapted to capture the plurality of image based on the dynamically rendered input.
  • the system further comprises of an administrator module, wherein created for an admin to provide access to a plurality of client users like an institute module, created to give access to plurality of Institutes of their login by admin based on the requirement; an uploader module, created to give access to plurality of uploaders to upload bulk images of their login by admin based on their requirement; an annotator module, created to give access to plurality of annotators of their login to annotate the individual images by admin based on the requirement; and a prediction module, created as a reporting tool that displays a disease based on the uploaded images using a convolutional network.
  • an administrator module wherein created for an admin to provide access to a plurality of client users like an institute module, created to give access to plurality of Institutes of their login by admin based on the requirement
  • an uploader module created to give access to plurality of uploaders to upload bulk images of their login by admin based on their requirement
  • an annotator module created to give access to plurality of annotators of their login to annotate the individual images by
  • the system detects at least one disease selected from such as diabetic retinopathy, glaucoma, TB, pneumonia, and breast cancerand other diseases related to pathological failure.
  • the system detects at least one of a lesion, a venous beading, a venous loop, an intra retinal microvascular abnormality, an 25 intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/prerenal hemorrhage, neovascularization, Lung Cancer, Breast Cancer, pulmonary diseases or the like.
  • a computer implemented method to detect a plurality of diseases from captured plurality of image comprises a step of providing, a plurality of image for analysis accessible by a user device via a graphical user interface of an image analysis module;
  • the method comprises another step of providing, a plurality of interactive elements on the graphical user interface of the plurality of image analysis module, the interactive elements configured to enable capture and analyze the plurality of images.
  • the computer implemented method comprises a step of receiving, an input from an image capturing device based on a plurality of parameters of the image capturing device by the plurality of image analysis application via the graphical user interface, wherein the input is the plurality of image of the patient displayed in a live mode.
  • the computer implemented method comprises a step of dynamically rendering, the input by the plurality of image analysis application via the graphical user interface, wherein the dynamically rendered input is configurable accessible on the graphical user interface via the user device using the interactive elements.
  • the computer implemented method comprises a step of capturing, the plurality of image based on the dynamically rendered input by the plurality of image analysis application via the graphical user interface; a step of identifying, a plurality of indicators throughout the plurality of image using a convolutional network; and a step of detecting, a presence or absence of a retinal disease based the identified indicators using the convolutional network; and a step of classifying, a severity of the disease based on the presence or absence of the disease using the convolutional network.
  • the computer implemented method deeds as an indicator is one of an abnormality, a retinal feature, and other indicators seen in the image of the patient to identify diseases such as TB/ Breast cancer, Lung cancer and other pulmonary diseasesln accordance with another embodiment of the invention, the computer implemented method identifies an abnormality is one of a lesion, a venous beading, a venous loop, an intra retinal microvascular abnormality, an intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/preretinal hemorrhage, neovascularization, Lung Cancer, Breast Cancer, pulmonary diseases or the like.
  • FIG. 1 illustrates a schematic layout of the institute login.
  • the members are sent invite to join the system.
  • the institute has authority to provide access or to revoke access. If the institute feels that there is no longer any need to continue access to a member, the institute can revoke the access to the particular member.
  • the institute has power to change the role of a member. For example, a member with end user access can be upgraded to annotator role to view additional information in the system. A single user can be assigned multiples roles in the system.
  • reference numeral 200 indicates the screen layout of the members tab under institute module.
  • the details of the members include name and image of the member. In an exemplary embodiment, details of 8 members are presented. There is no limitation on the number of members.
  • Reference numeral 222 indicates the members appearing on the screen layout.
  • a revoke tab is present on each member details tab. When the institute feels that there is no longer any need to provide the access to the member, the access may be revoked. In this way, a dynamic list of the members is maintained by the institute.
  • other tabs namely, send invite 208, edit role 210, dashboard 212, reports 214, institute logo 216, live feed 218 and referral response feed 220 are in inactive mode.
  • reference numeral 300 indicates the screen layout of the dashboard tab under institute module.
  • the dashboard tab 312 is created to view the summary of the information in an easy way.
  • a screen with information relating to patients 338, visits 340, images 342 and location 344 is presented.
  • the bad/good images 346 tab all the images uploaded are displayed.
  • DR/ No-DR images information relating DR and No-DR images is presented.
  • the images of various other diseases are presented .
  • detailed information of the visits is presented.
  • reference numeral 400 indicates the screen layout of the send invite tab under institute module.
  • email id 454 has to be entered.
  • Roles 456 have to selected from the give list of admin, doctor, uploader, annotator, enduser. It is possible to assign more than one roles to a member. After choosing the roles 456, invite 458 has to be sent.
  • FIG. 5 illustrates a report 500 generated by the system.
  • the report includes various parameters like visit time of the patient, location of the patient, MRD, DR status, DRISTi report and History of the member.
  • MRD number provided is 9538784595.
  • the image uploaded can be viewed by clicking on the view tab.
  • DRISTi report and DRDS report are also viewed by clicking on the respective view tab.
  • FIG. 6 illustrates a schematic layout of the doctor login according to an embodiment of the present invention.
  • the Dcotor ' s login module 600 includes Patients visits 602, MRD number 604, DR status 606, and Select patient visit date range 608. There is a separate tab for the Referrals 610. The detailed information can be viewed by clicking on any of the tabs. The number of tabs in the module can be increased or decreased based on the requirement.
  • reference numeral 700 indicates the screen layout of the patient visits tab under doctor module. By clicking on the patients visits 702, complete details of the patients are presented. By using the date filter, patients who visited on a particular date can be selected.
  • reference numeral 800 indicates the screen layout of the patient view tab under doctor module.
  • complete image of the eye 812 is presented.
  • the image of the eye 812 presented is of the left eye.
  • Patient history 814 is available at the top right hand side of the screen.
  • the menu tab includes various radio buttons namely DRO Normal, DR1 Mild, DR2 Moderate, DR3 Severe, DR4 Proliferative. An option is also present to save the selected radio button.
  • the lesion list includes No lesion found, Microaneurysm, Haemorrhage, Hard exudate, Soft exudate, Intratetinial microvascular abnormalities, New vessels on disc, Vitreal/ Pre-retinal haemorrhage, Veneous beading, Veneous loop, Lung Cancer using CT Scan, Breast Cancer using Mammogram, pulmonary diseases or the like.
  • FIG. 9 illustrates a schematic layout of the uploader module according to an embodiment of the present invention.
  • the top section includes tabs for Select Key space Retina, X ray, others 902, Select Data Range 904, Select Uploader 906 and Filter 908.
  • the middle section also includes the same tabs as in the top section but in a different order.
  • the bottom section includes Annotated Buckets 910 and Select Key Space 912.
  • reference numeral 1000 indicates the screen layout of the upload tab 1002 under uploader module.
  • the upload tab 1002 includes a tab to select an option from the image category. Ground truth file can be uploaded to the upload tab screen 1002. Similarly, zip file can be uploaded to the upload tab screen 1002.
  • FIG. 11 illustrates a schematic layout of the Annotator login module 1100 according to an embodiment of the present invention.
  • the Annotator's login module 1100 includes Select image category 1102, Upload the image 1104, Run machine predictions 1106 and History 1108. The detailed information can be viewed by clicking on any of the tabs. The number of tabs in the module can be increased or decreased based on the requirement.
  • reference numeral 1200 indicates the screen layout of the set bucket 1210 tab under annotator module. By selecting set bucket 1210 tab, the type of image either Retina or X-ray/ any other images has to be selected. It is possible to filter the data based on the uploaded date and the type of uploader.
  • FIG. 13 illustrates a schematic layout of the Predictor login module 1400 according to an embodiment of the present invention.
  • the Predictor login module 1100 includes Select Disease (DR, Glaucoma, TB, Pneumonia, Breast Cancer) 1402, Select Image (JPG, JPEG, PNG) 1404, Predict 1406, Display 1408 and Storage Device 1410. All the images of the system are stored in the storage device 1410.
  • reference numeral 1500 indicates the screen layout of the Predict tab.
  • the select disease tab includes Diabetic Retinopathy, Glaucoma, TB/ Pneumonia, Breast Cancer, Lung Cancer, Breast Cancer, pulmonary diseases or the like. After uploading the image of the eye of the member, the system can predict the disease of the member.

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  • Engineering & Computer Science (AREA)
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Abstract

La présente invention concerne un système et un procédé d'analyse de diagnostic à distance d'une pluralité d'images sur la base de l'algorithme d'apprentissage automatique. Le système mis en œuvre par ordinateur est entraîné pour détecter une pluralité de maladies à partir d'une pluralité d'images capturées. Le système comprend au moins un processeur et un ou plusieurs dispositifs de stockage configurés pour stocker des instructions configurées pour être exécutées par le ou les processeurs afin de faire en sorte que le système reçoive une pluralité d'images d'un patient, identifie une pluralité d'indicateurs dans la pluralité d'images à l'aide d'un réseau de convolution, et un support de stockage lisible par ordinateur non transitoire configuré pour stocker une pluralité d'applications d'analyse d'image.
PCT/IN2020/050542 2019-06-21 2020-06-20 Système et procédé de diagnostic à distance d'une pluralité d'images Ceased WO2020255171A1 (fr)

Applications Claiming Priority (2)

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IN201941024817 2019-06-21
IN201941024817 2019-06-21

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WO2020255171A1 true WO2020255171A1 (fr) 2020-12-24

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4254340B2 (ja) * 2002-07-17 2009-04-15 株式会社島津製作所 遠隔画像分析装置
WO2013112588A1 (fr) * 2012-01-23 2013-08-01 Duke University Système et procédé pour l'organisation et l'analyse d'images à distance
CN104699963A (zh) * 2015-03-03 2015-06-10 吴开东 一种远程自动影像分析系统
WO2019075410A1 (fr) * 2017-10-13 2019-04-18 Ai Technologies Inc. Diagnostic basé sur l'apprentissage profond et recommandation de maladies et de troubles ophtalmiques

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4254340B2 (ja) * 2002-07-17 2009-04-15 株式会社島津製作所 遠隔画像分析装置
WO2013112588A1 (fr) * 2012-01-23 2013-08-01 Duke University Système et procédé pour l'organisation et l'analyse d'images à distance
CN104699963A (zh) * 2015-03-03 2015-06-10 吴开东 一种远程自动影像分析系统
WO2019075410A1 (fr) * 2017-10-13 2019-04-18 Ai Technologies Inc. Diagnostic basé sur l'apprentissage profond et recommandation de maladies et de troubles ophtalmiques

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