Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma.

Journal of Gastroenterology and Hepatology
Hiroyoshi IwagamiTomohiro Tada

Abstract

Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system using deep learning for the detection of E/J cancers. A total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non-cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board-certified specialists (experts). The sensitivity, specificity, and accuracy of the AI system were 94%, 42%, and 66%, respectively, and that of the experts were 88%, 43%, and 63%, respectively. The sensitivity of the AI system was favorable, while its specificity for non-cancerous lesions was similar to that of the experts. Interobserver agreement among the experts for detecting superficial E/J was fair (Fleiss' kappa = 0.26, z = 20....Continue Reading

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Citations

Sep 17, 2020·Digestive Endoscopy : Official Journal of the Japan Gastroenterological Endoscopy Society·Kazuki SumiyamaNaoto Tamai
Oct 16, 2020·Digestive Diseases and Sciences·Taseen SyedTilak Shah
Dec 22, 2020·International Journal of Telemedicine and Applications·Maksut SenbekovIldar Fakhradiyev
Jun 23, 2021·Digestive Diseases and Sciences·Yutaka OkagawaYutaka Saito
Nov 6, 2021·Journal of Clinical Gastroenterology·Pierfrancesco VisaggiEdoardo Savarino
Feb 1, 2022·Alimentary Pharmacology & Therapeutics·Pierfrancesco VisaggiNicola de Bortoli

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