STTR Phase I: A Fully-Automated Endoscopic Scoring System for Ulcerative Colitis

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The broader/commercial impact of this STTR Phase 1 project will develop an automated system that significantly increases the speed, reliability, and accuracy of disease severity assessment in ulcerative colitis (UC) patients. This lifelong debilitating disease impacts almost 1 M US patients, and developing effective treatments requires accurate, reliable, and timely scoring. Currently, the FDA-approved primary diagnostic is the endoscopic component of the Mayo score, and clinical trials require a time-consuming, resource-constrained process of expert reading by specialized gastroenterologists. The proposed technology will serve multiple purposes: expediting the scoring process to determine patient eligibility for drug trials, measuring baseline and disease change to obtain FDA endpoints for UC drug trials, and providing insights that inform GI physicians on the effectiveness of a particular therapy for a specific patient. This system scoring – performed in minutes, rather than days – will improve efficiency and expedite recruitment and retention of trial participants (the greatest challenge in drug trials). With an estimated $56 M spent annually on expert reading of colonoscopy videos for drug trials, this technology would not only save time but would also significantly reduce costs. In addition, this methodology will translate into clinical care, providing community physicians with automated GI expertise and valuable insight into disease progression and patient response to therapy. Finally, this technology could be utilized as a teaching tool for medical students and GI residents. This STTR Phase I project is designed to create an automated system for video assessment of colonoscopies taken for UC monitoring. The unique innovative factor in this research is the automated processing of all data available from colonoscopy videos to create a reliable, repeatable, efficient, and quantitative assessment of the burden of UC disease. The approach uses a combination of an effective informative frame classifier, location estimation system, and disease severity classifier to generate scoring of the entire video. Algorithms for automated, comprehensive, machine-learning-based assessment of clinically-captured videos are the foundation of the system. The project will improve (1) classification accuracy between informative vs. non-informative video frames, (2) estimation of the camera location, and (3) validate the system against a heterogenous colonoscopy video dataset from multiple clinical providers and from colonoscopes from various manufacturers. The algorithms will be optimized for an endoscopic assessment and scoring system and extended through ongoing data collection with academic partners. This project will result in a novel approach to medical video analysis using effective machine learning methods to create a practical, data-driven solution for assessment and improvement of UC care.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.