Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis.

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Abstract

Deep learning is an innovative algorithm based on neural networks. Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small-bowel diseases. Manual examination of WCE is time consuming and can benefit from automatic detection using artificial intelligence (AI). We aimed to perform a systematic review of current literature pertaining to deep learning implementation in WCE.
We conducted a search in PubMed for all original publications on the subject of deep learning applications in WCE published between January 1, 2016, and December 15, 2019. Evaluation of the risk of bias was performed using tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted.
Of the 45 studies retrieved, 19 studies were included. All studies were retrospective. Deep learning applications for WCE included detection of ulcers, polyps, celiac disease, bleeding, and hookworm. Detection accuracy was above 90% for most studies and diseases. The pooled sensitivity and specificity for ulcer detection were 0.95 (95% CI, 0.89-0.98) and 0.94 (95% CI, 0.90-0.96), respectively. The pooled sensitivity and specificity for bleeding or bleeding source were 0.98 (95% CI, 0.96-0.99) and 0.99 (95% CI, 0.97-0.99), respectively.
Deep learning has achieved excellent performance for the detection of a range of diseases in WCE. Notwithstanding, current research is based on retrospective studies with a high risk of bias. Thus, future prospective multicenter studies are necessary in order for this technology to be implemented in the clinical use of WCE.
Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.