Methods for treatment of inflammatory bowel disease
AssigneeIterative Scopes, Inc.
InventorsJonathan Ng, Jean-Pierre Schott, Daniel Wang
Life of this patent
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Abstract
This specification describes systems and methods for obtaining various patient related data for inflammatory bowel disease (IBD). The methods and systems are configured for using machine learning to determine measurements of various characteristics and provide analysis related to IBD. The methods and systems may also obtain and incorporate electronic health data as well as other relevant data of patients along with endoscopic data to use for prediction IDB progression and recommending treatment.
Claims20
A method for predicting inflammatory bowel disease (IBD) activity in a patient and prescribing treatment for IBD, the method comprising:
- obtaining image data including endoscopic images of a gastrointestinal tract (GI) of a patient for a first time period and a second time period;
- determining one or more features to extract from the image data, the features each representing a physical parameter of the GI tract for each of the first time period and the second time period;
- extracting the one or more features from the image data to form a first feature vector for the first time period and a second feature vector for the second time period;
- selecting a machine learning model based on the one or more features included in the first feature vector and the second feature vector;
- processing the first feature vector and the second feature vector 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 progression of IBD based on the respective instances of the symptoms in the first and second vectors;
- determining, based on the processing, a prediction of disease progression of IBD in the patient;
- and
- storing, in a data store, the prediction in association with the image data.
A system for predicting inflammatory bowel disease (IBD) activity in a patient and prescribing treatment for IBD, the system comprising:
- at least one processor;
- and
- at least one memory storing instructions that, when executing by the at least one processor, cause the at least one processor to perform operations comprising:
- obtaining image data including endoscopic images of a gastrointestinal tract (GI) of a patient for a first time period and a second time period;
- determining one or more features to extract from the image data, the features each representing a physical parameter of the GI tract for each of the first time period and the second time period;
- extracting the one or more features from the image data to form a first feature vector for the first time period and a second feature vector for the second time period;
- selecting a machine learning model based on the one or more features included in the first feature vector and the second feature vector;
- processing the first feature vector and the second feature vector 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 progression of IBD based on the respective instances of the symptoms in the first and second vectors;
- determining, based on the processing, a prediction of disease progression of IBD in the patient;
- and
- storing, in a data store, the prediction in association with the image data.
One or more non-transitory computer readable media storing instructions predicting inflammatory bowel disease (IBD) activity in a patient and prescribing treatment for IBD, the instructions configured to cause at least one processor executing the instructions to perform operations comprising:
- obtaining image data including endoscopic images of a gastrointestinal tract (GI) of a patient for a first time period and a second time period;
- determining one or more features to extract from the image data, the features each representing a physical parameter of the GI tract for each of the first time period and the second time period;
- extracting the one or more features from the image data to form a first feature vector for the first time period and a second feature vector for the second time period;
- selecting a machine learning model based on the one or more features included in the first feature vector and the second feature vector;
- processing the first feature vector and the second feature vector 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 progression of IBD based on the respective instances of the symptoms in the first and second vectors;
- determining, based on the processing, a prediction of disease progression of IBD in the patient;
- and
- storing, in a data store, the prediction in association with the image data.
Dependencies are inferred from the claim text.