Colonoscopy Video Analysis Framework

Every year in Canada over 1.7 million patients are diagnosed with Ulcerative Colitis (UC), and have to go through colonoscopy procedures multiple times for disease detection and treatment monitoring. Trained clinicians use endoscopy facilities and technologies for colonoscopy procedures and unfortunately, the current error rate in disease detection is up to 20%. This project will build a framework that will analyze colonoscopy video streams in real-time, and offers the outcomes to support clinicians to accurately detect UC and monitor patient’s response to treatments.

Faculty Supervisor:

David Fleet

Student:

Micha Livne

Partner:

A.I. VALI

Discipline:

Computer science

Sector:

Life sciences

University:

University of Toronto

Program:

Accelerate

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