Methods and systems for automatically determining image characteristics serving as a basis for a diagnosis associated with an image study type

• US 10,311,566 B2
• Filed: 06/10/2016
• Issued: 06/04/2019
• 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, Michael W., Sati, Marwan
• Primary Examiner(s): Bali, Vikkram
• Application Number:
  US15/179,448
• Publication Number:
  US 20160364857A1
• Time in Patent Office: 1,089 Days
• Field of Search: None
• 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 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• 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 determining image characteristics serving as a basis for a diagnosis associated with an image study type. 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 an image study from the at least one data source over the interface. The image study being of the image study type and including a plurality of images. The electronic processor is also configured to determine an image characteristic for each of the plurality of images and determine whether each of the plurality of images was used to establish a diagnosis. The electronic processor is also configured to store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used to establish the diagnosis in a data structure.

Claims

1. A system of automatically determining image characteristics used to diagnosis a patient, the image characteristics associated with an image study type, wherein the image study type specifies an imaging procedure used to generate 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 an image study from the at least one data source over the interface, the image study being of the image study type and including a plurality of images,

determine an image characteristic for each of the plurality of images by at least one of a group consisting of determining whether each of the plurality of images includes an annotation and determining whether each of the plurality of images was displayed to a diagnosing physician rendering the diagnosis during a reading session of the diagnosing physician,

determine whether each of the plurality of images was used in diagnosing a patient,

create a data structure and store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used in diagnosing a patient in the data structure, and

modify a newly received image study of the same image study type as the first image study based upon the data structure by at least one of a group consisting of removing at least one image from the newly received image study that does not contain an image characteristic stored in the data structure and prioritizing images in the newly received image study for review.

2. The system of claim 1, wherein the image characteristic includes an anatomical structure.

3. The system of claim 1, wherein the image characteristic includes whether a contrast agent was used or a radioactive isotope was used.

4. The system of claim 1, wherein the image characteristic includes a time after a contrast agent or a radioactive isotope was introduced.

5. The system of claim 1, wherein the image characteristic includes an orientation.

6. The system of claim 1, wherein the image characteristic includes an image acquisition parameter.

7. The system of claim 1, wherein the image characteristic includes DICOM header information.

8. The system of claim 1, wherein the annotation includes a graphical marker designating a portion of an image.

9. The system of claim 1, wherein the annotation includes diagnostic information associated with an image.

10. The system of claim 1, wherein the electronic processor is configured to determine whether each of the plurality of images was used to establish the diagnosis by determining whether each of the plurality of images was diagnosed as normal.

11. The system of claim 1, wherein the electronic processor is further configured to automatically determine a frequency of an image with a particular image characteristic included in a subsequent image study of the image study type based on the data structure.

12. The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic including an annotation.

13. The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic not including an annotation.

14. The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic being displayed to a diagnosing physician.

15. The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic not being displayed to a diagnosing physician.

16. The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic being diagnosed as normal.

17. The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic not being diagnosed as normal.

18. The system of claim 11, wherein the electronic processor is further configured to generate a report based on the frequency.

19. The system of claim 18, wherein the report includes a plurality of frequencies, wherein each of the plurality of frequencies is associated with a different image characteristic.

20. The system of claim 18, wherein the plurality of frequencies includes frequencies satisfying at least one threshold.

21. The system of claim 20, wherein the at least one threshold is configurable based on one or more preferences associated with a user.

22. The system of claim 18, wherein the report includes plurality of frequencies, wherein each of the plurality of frequencies is associated with a different image study type.

23. The system of claim 18, wherein the report is parsable by at least one selected from a group consisting of an imaging modality, a diagnosing physician, an anatomical structure, patient demographic information, and a date.

24. The system of claim 1, wherein the electronic processor is further configured to modify a subsequent image study of the image study type to eliminate at least one image from being generated for the subsequent image study based on the data structure.

25. The system of claim 1, wherein the electronic processor is further configured to receive a plurality of image studies of the image study type and prioritize the plurality of image studies based on the data structure.