US 12,394,524 B2
Systems and methods for analysis of medical images for scoring of inflammatory bowel disease
Jonathan Ng, Cambridge, MA (US); Jean-Pierre Schott, Cambridge, MA (US); Perikumar Mukundbhai Javia, Cambridge, MA (US); Austin Wang, Cambridge, MA (US); Neelima Chavali, Cambridge, MA (US); Thomas Varner, Cambridge, MA (US); Lavi Erisson, Cambridge, MA (US); Sloane Allebes Phillips, Cambridge, MA (US); and Daniel Wang, Cambridge, MA (US)
Assigned to Iterative Scopes, Inc., Cambridge, MA (US)
Filed by Iterative Scopes, Inc., Cambridge, MA (US)
Filed on Feb. 1, 2021, as Appl. No. 17/164,418.
Claims priority of provisional application 63/055,125, filed on Jul. 22, 2020.
Prior Publication US 2022/0028547 A1, Jan. 27, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 50/20 (2018.01); A61B 1/31 (2006.01); A61B 5/00 (2006.01); G06F 18/21 (2023.01); G06F 18/24 (2023.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01); G16B 20/00 (2019.01); G16B 30/00 (2019.01); G16B 40/20 (2019.01); G16H 10/20 (2018.01); G16H 10/40 (2018.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01)
CPC G16H 50/20 (2018.01) [A61B 1/31 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); G06F 18/21 (2023.01); G06F 18/24 (2023.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G16B 20/00 (2019.02); G16B 30/00 (2019.02); G16B 40/20 (2019.02); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); G16H 70/60 (2018.01); G06T 2207/10068 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30092 (2013.01); G06V 2201/03 (2022.01); G16H 10/20 (2018.01); G16H 10/40 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A method for treatment of inflammatory bowel disease (IBD) in a patient and determining a score of disease activity, the method comprising:
obtaining image data including endoscopic images of a gastrointestinal tract (GI) of a patient;
determining one or more features to extract from the image data, the features each representing a physical parameter of the GI tract;
extracting the one or more features from the image data to form feature data;
selecting a machine learning model based on the one or more features included in the feature data;
processing the feature data using the machine learning model, the machine learning model being trained with labeled image data representing instances of symptoms of IBD being in the GI tract, the labeled image data associating scores representing a severity of IBD the respective instances of the symptoms, wherein processing the feature data includes:
generating one or more frame level annotations corresponding to one or more individual frames the image data; and
accessing context data representing analysis for IBD severity for one or more previous frames to the one or more frames or representing analysis for IBD severity for one or more subsequent frames of the one or more frames;
determining, based on the processing and based on the one or more frame level annotations and the context data, a score representing a severity of IBD in the patient indicated by the image data; and
storing, in a data store, the score in association with the image data.