AI analysis and modified type classification for endocytoscopic observation of esophageal lesions

Abstract

Abstract Endocytoscopy (EC) facilitates real-time histological diagnosis of esophageal lesions in vivo. We developed a deep-learning artificial intelligence (AI) system for analysis of EC images and compared its diagnostic ability with that of an expert pathologist and nonexpert endoscopists. Our new AI was based on a vision transformer model (DeiT) and trained using 7983 EC images of the esophagus (2368 malignant and 5615 nonmalignant). The AI evaluated 114 randomly arranged EC pictures (33 ESCC and 81 nonmalignant lesions) from 38 consecutive cases. An expert pathologist and two nonexpert endoscopists also analyzed the same image set according to the modified type classification (adding four EC features of nonmalignant lesions to our previous classification). The area under the curve calculated from the receiver-operating characteristic curve for the AI analysis was 0.92. In per-image analysis, the overall accuracy of the AI, pathologist, and two endoscopists was 91.2%, 91.2%, 85.9%, and 83.3%, respectively. The kappa value between the pathologist and the AI, and between the two endoscopists and the AI showed moderate concordance; that between the pathologist and the two endoscopists showed poor concordance. In per-patient analysis, the overall accuracy of the AI, pathologist, and two endoscopists was 94.7%, 92.1%, 86.8%, and 89.5%, respectively. The modified type classification aided high overall diagnostic accuracy by the pathologist and nonexpert endoscopists. The diagnostic ability of the AI was equal or superior to that of the experienced pathologist. AI is expected to support endoscopists in diagnosing esophageal lesions based on EC images.