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
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Abstract	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 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. 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). 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. [Display omitted]
AbstractList	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 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. 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). 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. [Display omitted] 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 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. 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). 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. 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.BACKGROUND AND AIMSThe 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 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.METHODSWe 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.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).RESULTSOverall, 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).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.CONCLUSIONSDeep 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.
Author	Eliakim, Rami Ben-Horin, Shomron Barash, Yiftach Margalit, Reuma Yehuda Amitai, Marianne Michal Klang, Eyal Shimon, Orit Kopylov, Uri Soffer, Shelly Albshesh, Ahmad
Author_xml	– sequence: 1 givenname: Eyal surname: Klang fullname: Klang, Eyal organization: Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel – sequence: 2 givenname: Yiftach surname: Barash fullname: Barash, Yiftach organization: DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel – sequence: 3 givenname: Reuma Yehuda surname: Margalit fullname: Margalit, Reuma Yehuda organization: Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel – sequence: 4 givenname: Shelly orcidid: 0000-0002-7853-2029 surname: Soffer fullname: Soffer, Shelly organization: Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel – sequence: 5 givenname: Orit surname: Shimon fullname: Shimon, Orit organization: Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel – sequence: 6 givenname: Ahmad surname: Albshesh fullname: Albshesh, Ahmad organization: Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel – sequence: 7 givenname: Shomron surname: Ben-Horin fullname: Ben-Horin, Shomron organization: Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel – sequence: 8 givenname: Marianne Michal surname: Amitai fullname: Amitai, Marianne Michal organization: Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel – sequence: 9 givenname: Rami surname: Eliakim fullname: Eliakim, Rami organization: Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel – sequence: 10 givenname: Uri surname: Kopylov fullname: Kopylov, Uri organization: Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel
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Copyright	2020 American Society for Gastrointestinal Endoscopy Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.
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Snippet	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... 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...
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SubjectTerms	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
Title	Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy
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