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

BACKGROUND AND AIMS Conventional endoscopy for the early detection of esophageal and esophagogastric junctional (EGJ) 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 1,172 images from 166 pathologically proven superficial E/J cancer cases and 2,271 images of normal mucosa in EGJ 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.