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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.
David Fleet
Micha Livne
A.I. VALI
Computer science
Life sciences
University of Toronto
Accelerate
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