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Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy.
Klang, Eyal; Barash, Yiftach; Margalit, Reuma Yehuda; Soffer, Shelly; Shimon, Orit; Albshesh, Ahmad; Ben-Horin, Shomron; Amitai, Marianne Michal; Eliakim, Rami; Kopylov, Uri.
Afiliação
  • Klang E; Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel.
  • Barash Y; DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel.
  • Margalit RY; Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Soffer S; Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel; Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; DeepVision Lab, Sheba Medical Center, Tel Hashomer, Israel.
  • Shimon O; Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel.
  • Albshesh A; Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Ben-Horin S; Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Amitai MM; Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel.
  • Eliakim R; Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
  • Kopylov U; Department of Gastroenterology, Sheba Medical Center, Tel Hashomer, Israel.
Gastrointest Endosc ; 91(3): 606-613.e2, 2020 03.
Article em En | MEDLINE | ID: mdl-31743689
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Úlcera / Doença de Crohn / Endoscopia por Cápsula / Aprendizado Profundo / Intestino Delgado Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Úlcera / Doença de Crohn / Endoscopia por Cápsula / Aprendizado Profundo / Intestino Delgado Idioma: En Ano de publicação: 2020 Tipo de documento: Article