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

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. We retrospectively collected CE images of known CD patients and control subjects. Each image w...

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Veröffentlicht in:	Gastrointestinal endoscopy Jg. 91; H. 3; S. 606 - 613.e2
Hauptverfasser:	Klang, Eyal, Barash, Yiftach, Margalit, Reuma Yehuda, Soffer, Shelly, Shimon, Orit, Albshesh, Ahmad, Ben-Horin, Shomron, Amitai, Marianne Michal, Eliakim, Rami, Kopylov, Uri
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.03.2020
Schlagworte:	Algorithms Automation Capsule Endoscopy - methods Crohn Disease - complications Crohn Disease - 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 Ulcer - etiology AI CD t-SNE CE CNN AUC
ISSN:	0016-5107, 1097-6779, 1097-6779
Online-Zugang:	Volltext
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