Premium
Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma
Author(s) -
Iwagami Hiroyoshi,
Ishihara Ryu,
Aoyama Kazuharu,
Fukuda Hiromu,
Shimamoto Yusaku,
Kono Mitsuhiro,
Nakahira Hiroko,
Matsuura Noriko,
Shichijo Satoki,
Kanesaka Takashi,
Kanzaki Hiromitsu,
Ishii Tatsuya,
Nakatani Yasuki,
Tada Tomohiro
Publication year - 2021
Publication title -
journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.214
H-Index - 130
eISSN - 1440-1746
pISSN - 0815-9319
DOI - 10.1111/jgh.15136
Subject(s) - medicine , esophagogastric junction , cancer , asymptomatic , adenocarcinoma , esophageal cancer , endoscopy , esophageal adenocarcinoma , radiology , pathology , artificial intelligence , computer science
Background and Aim 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. Methods 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). Results 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 = 20.4, P < 0.001). Conclusions Our AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers.