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.
|Original language||English (US)|
|Title of host publication||Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020|
|Editors||Alba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jul 2020|
|Event||33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States|
Duration: Jul 28 2020 → Jul 30 2020
|Name||Proceedings - IEEE Symposium on Computer-Based Medical Systems|
|Conference||33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020|
|Period||7/28/20 → 7/30/20|
Bibliographical noteFunding Information:
This work was supported in part by the National Institutes of Health Grant No. #1R01DK106130-01A1. Findings, opinions, and conclusions expressed in this paper do not necessarily reflect the view of the funding agency.
© 2020 IEEE.
- Convolution neural network (CNN)
- Multi-center clinical trial
- Real-time feedback of colonoscopy quality