Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy.

Abstract

BACKGROUND AND AIMS:

The aim of our study was to develop and evaluate a deep learning algorithm for the automated detection of small-bowel ulcers in Crohn's disease (CD) on capsule endoscopy (CE) images of individual patients.

METHODS:

We retrospectively collected CE images of known CD patients and control subjects. Each image was labeled by an expert gastroenterologist as either normal mucosa or containing mucosal ulcers. A convolutional neural network was trained to classify images into either normal mucosa or mucosal ulcers. First, we trained the network on 5-fold randomly split images (each fold with 80% training images and 20% images testing). We then conducted 10 experiments in which images from n - 1 patients were used to train a network and images from a different individual patient were used to test the network. Results of the networks were compared for randomly split images and for individual patients. Area under the curves (AUCs) and accuracies were computed for each individual network.

RESULTS:

Overall, our dataset included 17,640 CE images from 49 patients: 7391 images with mucosal ulcers and 10,249 images of normal mucosa. For randomly split images results were excellent, with AUCs of .99 and accuracies ranging from 95.4% to 96.7%. For individual patient-level experiments, the AUCs were also excellent (.94-.99).

CONCLUSIONS:

Deep learning technology provides accurate and fast automated detection of mucosal ulcers on CE images. Individual patient-level analysis provided high and consistent diagnostic accuracy with shortened reading time; in the future, deep learning algorithms may augment and facilitate CE reading.

• Authors: Eyal Klang, Yiftach Barash, Reuma Yehuda Margalit, Shelly Soffer, Orit Shimon, Ahmad Albshesh, Shomron Ben-Horin, Marianne Michal Amitai, Rami Eliakim, Uri Kopylov
• Journal: Gastrointestinal endoscopy (Gastrointest Endosc) Vol. 91 Issue 3 Pg. 606-613.e2 (03 2020) ISSN: 1097-6779 [Electronic] United States
• PMID: 31743689 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Topics

• Algorithms
• Automation
• Capsule Endoscopy (methods)
• Crohn Disease (complications, diagnostic imaging)
• Deep Learning
• Humans
• Intestinal Mucosa (diagnostic imaging)
• Intestine, Small (diagnostic imaging)
• Neural Networks, Computer
• Random Allocation
• Reproducibility of Results
• Retrospective Studies
• Ulcer (diagnostic imaging, etiology)