Methods and systems for automatically analyzing clinical images using rules and image analytics

• US 10,360,675 B2
• Filed: 06/10/2016
• Issued: 07/23/2019
• Est. Priority Date: 06/12/2015
• Status: Active Grant

• Current Assignee: Merative US LP
• Original Assignee: International Business Machines Corporation
• Inventors: Reicher, Murray A., DeVries, Jon T., Ferro, Jr., Michael W., Sati, Marwan
• Primary Examiner(s): Lu, Tom Y
• Application Number:
  US15/179,506
• Publication Number:
  US 20160364631A1
• Time in Patent Office: 1,138 Days
• CPC Class Codes: A61B 10/02 Instruments for taking cell samples or for biopsy A61B10/0038 and A61B10/0045 take precedence; needle locating or guiding means A61B17/3403; samplers for enzymology or microbiology C12M1/26; sampling or preparing biological specimens G01N33/48
• CPC Class Codes: A61B 2034/108 Computer aided selection or customisation of medical implants or cutting guides
• CPC Class Codes: A61B 2090/373 using light, e.g. by using optical scanners
• CPC Class Codes: A61B 2090/374 NMR or MRI
• CPC Class Codes: A61B 2090/3762 using computed tomography systems [CT]
• CPC Class Codes: A61B 2090/378 using ultrasound
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/015 By temperature mapping of body part
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4312 Breast evaluation or disorder diagnosis A61B5/0091 takes precedence
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06F 18/285 Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0488 : using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06F 40/169 : Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/205 : Parsing
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06T 11/008 : Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/30004 : Biomedical image processing
• CPC Class Codes: G06T 2207/30008 : Bone
• CPC Class Codes: G06T 2207/30016 : Brain
• CPC Class Codes: G06T 2207/30052 : Implant; Prosthesis
• CPC Class Codes: G06T 2207/30061 : Lung
• CPC Class Codes: G06T 2207/30068 : Mammography; Breast
• CPC Class Codes: G06T 2207/30088 : Skin; Dermal
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/0014 : using an image reference approach
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 2201/031 : of internal organs
• CPC Class Codes: G16H 10/20 : for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 : relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Methods and systems for automatically analyzing clinical images using rules and image analytics. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive training information from the at least one data source over the interface. The training information includes a plurality of images and graphical reporting associated with each of the plurality of images. The electronic processor is also configured to perform machine learning to develop a model using the training information and receive an image for analysis. The electronic processor is also configured to determine a set of rules for the image and automatically process the image using the model and the set of rules to generate a diagnosis for the image.

Claims

1. A system for automatically analyzing clinical images using rules and image analytics developed using graphical reporting associated with previously-analyzed clinical images, the system comprising:

a server including an electronic processor and an interface for communicating with at least one data source, the electronic processor configured to

receive training information from the at least one data source over the interface, the training information including a plurality of images and graphical reporting associated with each of the plurality of images, each graphical reporting including a graphical marker designating a portion of one of the plurality of images and diagnostic information associated with the portion of the one of the plurality of images,

perform machine learning to develop a model using the training information,

receive an image for analysis,

determine a set of rules for the image, and

automatically process the image using the model and the set of rules to generate a diagnosis for the image.

2. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on a user viewing the image within an image review application.

3. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on a diagnosing physician performing a diagnosis of the image.

4. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on a physician ordering an image procedure during which the image was generated.

5. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on patient demographic information associated with the image.

6. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on an organization of diagnosing physicians associated with the image.

7. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on an organization of healthcare facilities associated with the image.

8. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on an organization associated with a workstation displaying the image.

9. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on a type of the image or a type of an imaging exam associated with the image.

10. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on a geographic location associated with the image.

11. The system of claim 10, wherein the geographic location includes a geographic location of a diagnosing physician associated with the image.

12. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on an imaging modality generating the image.

13. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on an image acquisition site associated with the image.

14. The system of claim 1, wherein the electronic processor is configured to determine the set of rules based on applicable epidemic information.

15. The system of claim 1, wherein the electronic processor is further configured to determine an anatomical structure represented in the image and wherein the electronic processor is configured to determine the set of rules based on the anatomical structure.

16. The system of claim 15, wherein the electronic processor is configured to determine the anatomical structure using the model.

17. The system of claim 1, wherein the electronic processor is further configured to generate a graphical user interface for displaying and modifying the set of rules.