Automatic detection of various abnormalities in capsule endoscopy videos by a deep learning-based system: a multicenter study.

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Abstract

A deep convolutional neural network (CNN) system could be a high-level screening tool for capsule endoscopy (CE) reading but has not been established for targeting various abnormalities. We aimed to develop a CNN-based system and compare it with the existing QuickView mode in terms of their abilities to detect various abnormalities.
We trained a CNN system using 66,028 CE images (44,684 images of abnormalities and 21,344 normal images). The detection rate of the CNN for various abnormalities was assessed per patient, using an independent test set of 379 consecutive small-bowel CE videos from 3 institutions. Mucosal breaks, angioectasia, protruding lesions, and blood content were present in 94, 29, 81, and 23 patients, respectively. The detection capability of the CNN was compared with that of the QuickView mode.
The CNN picked up 1,135,104 images (22.5%) from the 5,050,226 test images, and thus, the sampling rate of the QuickView mode was set to 23% in this study. In total, the detection rate of the CNN for abnormalities per patient was significantly higher than that of the QuickView mode (99% vs 89%, p<0.001). In detail, the detection rates of the CNN for mucosal breaks, angioectasia, protruding lesions, and blood content were 100% (94/94), 97% (28/29), 98% (80/81), and 100% (23/23), respectively, and those of the QuickView mode were 91%, 97%, 80%, and 96%, respectively.
We developed and tested a CNN-based detection system for various abnormalities, using multicenter CE videos. This system could serve as an alternative high-level screening tool to the QuickView mode.
Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.