Real-Time Feedback for Colonoscopy in a Multicenter Clinical Trial

Date
2020-01-01
Authors
Tavanapong, Wallapak
Oh, JungHwan
Kijkul, Gavin
Pratt, Jacob
Wong, Johnny
deGroen, Piet
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Altmetrics
Authors
Research Projects
Organizational Units
Computer Science
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Abstract

We report the technical challenges, solutions, and lessons learned from deploying real-time feedback systems in three hospitals as part of a multi-center controlled clinical trial to improve quality of colonoscopy. Previous clinical trials were conducted in one center. The technical challenges for our multicenter clinical trial include 1) reducing additional work by the endoscopists to utilize real-time feedback, 2) handling different colonoscopy practices at different hospitals, and 3) training an effective CNN-based classification model with a large variety of patterns of data in day-to-day colonoscopy practice. We report performance of our real-time systems over a period of 20 weeks at each hospital. We conclude that CNN-based classification can achieve very good performance in real-world deployment when trained with high quality data.

Description

This is a manuscript of a proceeding published as W. Tavanapong, J. Oh, G. Kijkul, J. Pratt, J. Wong and P. deGroen, "Real-Time Feedback for Colonoscopy in a Multicenter Clinical Trial," 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, 2020, pp. 13-18, doi: 10.1109/CBMS49503.2020.00010.

Keywords
Multi-center clinical trial, Real-time feedback of colonoscopy quality, Convolution Neural Network (CNN)
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