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CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(11): E1778-E1784
DOI: 10.1055/a-1546-8266
Original article

Automated detection of cecal intubation with variable bowel preparation using a deep convolutional neural network

Daniel J. Low
1   St. Michael’s Hospital, University of Toronto
,
Zhuoqiao Hong
2   Massachusetts Institute of Technology
,
Rishad Khan
1   St. Michael’s Hospital, University of Toronto
,
Rishi Bansal
1   St. Michael’s Hospital, University of Toronto
,
Nikko Gimpaya
1   St. Michael’s Hospital, University of Toronto
,
Samir C. Grover
1   St. Michael’s Hospital, University of Toronto
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Abstract

Background and study aims Colonoscopy completion reduces post-colonoscopy colorectal cancer. As a result, there have been attempts at implementing artificial intelligence to automate the detection of the appendiceal orifice (AO) for quality assurance. However, the utilization of these algorithms has not been demonstrated in suboptimal conditions, including variable bowel preparation. We present an automated computer-assisted method using a deep convolutional neural network to detect the AO irrespective of bowel preparation.

Methods A total of 13,222 images (6,663 AO and 1,322 non-AO) were extracted from 35 colonoscopy videos recorded between 2015 and 2018. The images were labelled with Boston Bowel Preparation Scale scores. A total of 11,900 images were used for training/validation and 1,322 for testing. We developed a convolutional neural network (CNN) with a DenseNet architecture pre-trained on ImageNet as a feature extractor on our data and trained a classifier uniquely tailored for identification of AO and non-AO images using binary cross entropy loss.

Results The deep convolutional neural network was able to correctly classify the AO and non-AO images with an accuracy of 94 %. The area under the receiver operating curve of this neural network was 0.98. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 0.96, 0.92, 0.92 and 0.96, respectively. AO detection was > 95 % regardless of BBPS scores, while non-AO detection improved from BBPS 1 score (83.95 %) to BBPS 3 score (98.28 %).

Conclusions A deep convolutional neural network was created demonstrating excellent discrimination between AO from non-AO images despite variable bowel preparation. This algorithm will require further testing to ascertain its effectiveness in real-time colonoscopy.



Publication History

Received: 05 February 2021

Accepted: 04 June 2021

Article published online:
12 November 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
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