A deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison.
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Abstract
Endoscopic differential diagnoses of gastric mucosal lesions (benign gastric ulcer, early gastric cancer [EGC], and advanced gastric cancer) remain challenging. We aimed to develop and validate convolutional neural network-based artificial intelligence (AI) models: lesion detection (AI-LD), differential diagnosis (AI-DDx), and invasion-depth (AI-ID, pT1a vs. pT1b among EGC) models.This study included 1,366 consecutive patients with gastric mucosal lesions from 2 referral centers in Korea. One representative endoscopic image from each patient was used. Histological diagnoses were set as the criterion standard. The performances of the AI-DDx (training/internal/external validation set, n=1009/112/245) and AI-ID (training/internal/external validation set, n=620/68/155) were compared with visual diagnoses by independent endoscopists (stratified by novice [<1 year of experience], intermediate [2-3 years of experience], and expert [>5 years of experience]) and endoscopic ultrasonography (EUS) results, respectively.The AI-DDx showed good diagnostic performance for both internal (area under of the receiver operating characteristic curve [AUROC]=0.86) and external validation (AUROC=0.86). The performance of the AI-DDx was better than that of the novice (AUROC=0.82, P=0.01) and intermediate endoscopists (AUROC=0.84, P=0.02), but was comparable to the experts (AUROC=0.89, P=0.12) in the external validation set. The AI-ID showed fair performances in both internal (AUROC=0.78) and external validation sets (AUROC=0.73), which were significantly better than EUS results performed by experts (internal validation: AUROC=0.62, external validation: AUROC=0.56; both P <0.001).The AI-DDx was comparable with experts and outperformed novice and intermediate endoscopists for the differential diagnosis of gastric mucosal lesion. The AI-ID performed better than EUS for the invasion-depth evaluation (https://aiscopeseoul.com/).Copyright © 2021 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.