Chest x-ray differential diagnosis system

• US 10,541,050 B2
• Filed: 10/24/2018
• Issued: 01/21/2020
• Est. Priority Date: 05/25/2017
• Status: Active Grant

• Current Assignee: Enlitic, Inc.
• Original Assignee: Enlitic, Inc.
• Inventors: Lyman, Kevin, Bernard, Devon, Yao, Li, Almeida, Diogo, Covington, Ben, Upton, Anthony
• Primary Examiner(s): Tsai, Tsung Yin
• Application Number:
  US16/168,866
• Publication Number:
  US 20190066835A1
• Time in Patent Office: 454 Days
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/4233 using matrix detectors
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/468 allowing annotation or message recording
• CPC Class Codes: A61B 6/50 specially adapted for specific body parts; specially adapted for specific clinical applications
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/505 for diagnosis of bone
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/5288 involving retrospective matching to a physiological signal
• CPC Class Codes: A61B 6/5294 involving using additional data, e.g. patient information, image labeling, acquisition parameters
• CPC Class Codes: A61B 6/563 involving image data transmission via a network
• CPC Class Codes: A61B 8/468 allowing annotation or message recording
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/565 involving data transmission via a network
• CPC Class Codes: G01T 1/247 Detector read-out circuitry for processing gain or off-set correction H04N
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 3/048 : Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/167 : Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G06F 40/169 : Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/197 : Version control for software G06F8/71
• CPC Class Codes: G06F 40/247 : Thesauruses; Synonyms
• CPC Class Codes: G06F 40/279 : Recognition of textual entities
• CPC Class Codes: G06F 40/30 : Semantic analysis
• CPC Class Codes: G06F 40/56 : Natural language generation
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/04 : Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/084 : Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/10 : Office automation; Time management
• CPC Class Codes: G06Q 10/103 : Workflow collaboration or project management
• CPC Class Codes: G06Q 50/22 : Social work or social welfare, e.g. community support activities or counselling services
• CPC Class Codes: G06T 11/003 : Reconstruction from projections, e.g. tomography
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10081 : Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 : Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10116 : X-ray image
• CPC Class Codes: G06T 2207/10132 : Ultrasound image
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/30004 : Biomedical image processing
• CPC Class Codes: G06T 2207/30061 : Lung
• CPC Class Codes: G06T 2207/30068 : Mammography; Breast
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/0016 : involving temporal comparison
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06V 10/751 : Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 10/761 : Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 10/98 : Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
• CPC Class Codes: G06V 2201/03 : Recognition of patterns in medical or anatomical images
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 : for processing medical images, e.g. editing
• CPC Class Codes: G16H 40/20 : for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04N 5/32 : Transforming X-rays cameras or camera modules for generating image signals from X-rays H04N23/30; circuitry of SSIS for transforming X-rays into image signals H04N25/30

Abstract

A chest x-ray differential diagnosis system is operable to generate abnormality pattern data is generated for each of a received plurality of chest x-rays by identifying at least one pattern in each chest x-ray corresponding to an abnormality by utilizing a computer vision model that is trained on a plurality of training chest x-rays. Differential diagnosis data is generated for each chest x-ray based on the abnormality pattern data. Filtering parameters are received from a client device, and a filtered chest x-ray queue that includes a subset of chest x-rays is selected based on the filtering parameters and the differential diagnosis data is generated for transmission to the client device for display. Differential diagnosis data corresponding a chest x-ray indicated in chest x-ray selection data received from the client device is transmitted to the client device for display via the display device in conjunction with the chest x-ray.

Claims

1. A medical image differential diagnosis system, comprising:

a processing system that includes a processor; and

a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising;

receiving a plurality of medical images via a network;

generating abnormality pattern data for each of the medical images by identifying at least one pattern in the medical images corresponding to an abnormality by utilizing a computer vision model, wherein the computer vision model is trained on a plurality of training medical images;

generating differential diagnosis data for each of the plurality of medical images based on the abnormality pattern data;

receiving, via the network, filtering parameters in response to a first prompt via an interactive interface displayed on a display device associated with a user of the medical image differential diagnosis system;

generating a filtered medical image queue for transmission to a client device associated with the user for display via the interactive interface, wherein the filtered medical image queue includes a proper subset of medical images selected from the plurality of medical images based on the filtering parameters and the differential diagnosis data;

receiving, via the network, medical image selection data indicating one of the proper subset of medical images in response to a second prompt via the interactive interface;

transmitting the differential diagnosis data to the client device via the network corresponding to one of the plurality of medical images indicated in the medical image selection data for display via the display device in conjunction with the one of the plurality of medical images.

2. The medical image differential diagnosis system of claim 1, wherein the plurality of medical images includes a normal subset of medical images with corresponding differential diagnosis data indicating a normal diagnosis, and wherein the proper subset of medical images is selected to automatically omit medical images in the normal subset of medical images.

3. The medical image differential diagnosis system of claim 1, wherein the differential diagnosis data for each of the plurality of medical images includes a plurality of binary values indicating whether each of a plurality of abnormality pattern types are one of:

present or not present based on the abnormality pattern data.

4. The medical image differential diagnosis system of claim 3, wherein the abnormality pattern data includes confidence score data corresponding to each of the plurality of abnormality pattern types, wherein generating the differential diagnosis data includes comparing the confidence score data for each of the plurality of abnormality pattern types to a first confidence score threshold, wherein a first one of the plurality of binary values indicates a corresponding first one of the plurality of abnormality pattern types is present when the corresponding confidence score data compares favorably to the first confidence score threshold, and wherein a second one of the plurality of binary values indicates a corresponding second one of the plurality of abnormality pattern types is present when the corresponding confidence score data compares unfavorably to the first confidence score threshold.

5. The medical image differential diagnosis system of claim 4, wherein the operations further comprise:

receiving, via the network, a new confidence score threshold in response to a third prompt displayed via the interactive interface;

generating updated differential diagnosis data by applying the new confidence score threshold; and

generating an updated filtered medical image queue for transmission to the client device for display via the interactive interface by generating a new proper subset of medical images based on the updated differential diagnosis data.

6. The medical image differential diagnosis system of claim 3, wherein the filtering parameters indicate a subset of the plurality of abnormality pattern types, and wherein generating the filtered medical image queue includes selecting ones of the plurality of medical images with differential diagnosis data that indicates that at least one of the subset of the plurality of abnormality pattern types in the filtering parameters is present to be included in the proper subset of medical images.

7. The medical image differential diagnosis system of claim 3, wherein the plurality of abnormality pattern types includes at least one of:

cardiomegaly, consolidation, effusion, emphysema, or fracture.

8. The medical image differential diagnosis system of claim 1, wherein the plurality of medical images are ordered based on priority data.

9. The medical image differential diagnosis system of claim 1, wherein the operations further comprise:

receiving, via the network, corrected diagnosis data in response to a third prompt via the interactive interface in conjunction with the display of the one of the plurality of medical images and the differential diagnosis data; and

mapping the corrected diagnosis data to the one of the plurality of medical images in a medical image database.

10. The medical image differential diagnosis system of claim 1, wherein the plurality of medical images are mapped to known diagnosis data in a medical image database, and wherein the operations further comprise:

generating a similarity score by comparing the known diagnosis data to the differential diagnosis data; and

generating accuracy data by comparing the similarity score to an accuracy threshold, wherein the accuracy data indicates that a discrepancy is present when the similarity score compares unfavorably to the accuracy threshold, and wherein the accuracy data indicates that no discrepancy is present when the similarity score compares favorably to the accuracy threshold;

wherein generating the filtered medical image queue includes selecting only medical images for the proper subset that have corresponding accuracy data indicating that a discrepancy is present.

11. A method for execution by a medical image differential diagnosing system that includes a processor, the method comprising:

receiving a plurality of medical images via a network;

generating abnormality pattern data for each of the medical images by identifying at least one pattern in the medical images corresponding to an abnormality by utilizing a computer vision model, wherein the computer vision model is trained on a plurality of training medical images;

generating differential diagnosis data for each of the plurality of medical images based on the abnormality pattern data;

receiving, via the network, filtering parameters in response to a first prompt via an interactive interface displayed on a display device associated with a user of the medical image differential diagnosis system;

generating a filtered medical image queue for transmission to a client device associated with the user for display via the interactive interface, wherein the filtered medical image queue includes a proper subset of medical images selected from the plurality of medical images based on the filtering parameters and the differential diagnosis data;

receiving, via the network, medical image selection data indicating one of the proper subset of medical images in response to a second prompt via the interactive interface;

transmitting the differential diagnosis data to the client device via the network corresponding to one of the plurality of medical images indicated in the medical image selection data for display via the display device in conjunction with the one of the plurality of medical images.

12. The method of claim 11, wherein the plurality of medical images includes a normal subset of medical images with corresponding differential diagnosis data indicating a normal diagnosis, and wherein the proper subset of medical images is selected to automatically omit medical images in the normal subset of medical images.

13. The method of claim 11, wherein the differential diagnosis data for each of the plurality of medical images includes a plurality of binary values indicating whether each of a plurality of abnormality pattern types are one of:

present or not present based on the abnormality pattern data.

14. The method of claim 13, wherein the abnormality pattern data includes confidence score data corresponding to each of the plurality of abnormality pattern types, wherein generating the differential diagnosis data includes comparing the confidence score data for each of the plurality of abnormality pattern types to a first confidence score threshold, wherein a first one of the plurality of binary values indicates a corresponding first one of the plurality of abnormality pattern types is present when the corresponding confidence score data compares favorably to the first confidence score threshold, and wherein a second one of the plurality of binary values indicates a corresponding second one of the plurality of abnormality pattern types is present when the corresponding confidence score data compares unfavorably to the first confidence score threshold.

15. The method of claim 14, further comprising:

receiving, via the network, a new confidence score threshold in response to a third prompt displayed via the interactive interface;

generating updated differential diagnosis data by applying the new confidence score threshold; and

generating an updated filtered medical image queue for transmission to the client device for display via the interactive interface by generating a new proper subset of medical images based on the updated differential diagnosis data.

16. The method of claim 13, wherein the filtering parameters indicate a subset of the plurality of abnormality pattern types, and wherein generating the filtered medical image queue includes selecting ones of the plurality of medical images with differential diagnosis data that indicates that at least one of the subset of the plurality of abnormality pattern types in the filtering parameters is present to be included in the proper subset of medical images.

17. The method of claim 13, wherein the plurality of abnormality pattern types includes at least one of:

cardiomegaly, consolidation, effusion, emphysema, or fracture.

18. The method of claim 11, further comprising:

receiving, via the network, corrected diagnosis data in response to a third prompt via the interactive interface in conjunction with the display of the one of the plurality of medical images and the differential diagnosis data; and

mapping the corrected diagnosis data to the one of the plurality of medical images in a medical image database.

19. The method of claim 11, wherein the plurality of medical images are mapped to known diagnosis data in a medical image database, further comprising:

generating a similarity score by comparing the known diagnosis data to the differential diagnosis data; and

generating accuracy data by comparing the similarity score to an accuracy threshold, wherein the accuracy data indicates that a discrepancy is present when the similarity score compares unfavorably to the accuracy threshold, and wherein the accuracy data indicates that no discrepancy is present when the similarity score compares favorably to the accuracy threshold;

wherein generating the filtered medical image queue includes selecting only medical images for the proper subset that have corresponding accuracy data indicating that a discrepancy is present.

20. An article of manufacture that includes a tangible storage medium that stores operational instructions, that when executed by a processor, causes the processor to:

receive a plurality of medical images via a network;

generate abnormality pattern data for each of the medical images by identifying at least one pattern in the medical images corresponding to an abnormality by utilizing a computer vision model, wherein the computer vision model is trained on a plurality of training medical images;

generate differential diagnosis data for each of the plurality of medical images based on the abnormality pattern data;

receive, via the network, filtering parameters in response to a first prompt via an interactive interface displayed on a display device;

generate a filtered medical image queue for transmission to a client device associated with the display device for display via the interactive interface, wherein the filtered medical image queue includes a proper subset of medical images selected from the plurality of medical images based on the filtering parameters and the differential diagnosis data;

receive, via the network, medical image selection data indicating one of the proper subset of medical images in response to a second prompt via the interactive interface;

transmit the differential diagnosis data to the client device via the network corresponding to one of the plurality of medical images indicated in the medical image selection data for display via the display device in conjunction with the one of the plurality of medical images.