Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison - 14/01/22
Abstract |
Background and Aims |
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, differential diagnosis (AI-DDx), and invasion depth (AI-ID; pT1a vs pT1b among EGC) models.
Methods |
This study included 1366 consecutive patients with gastric mucosal lesions from 2 referral centers in Korea. One representative endoscopic image from each patient was used. Histologic diagnoses were set as the criterion standard. Performance of the AI-DDx (training/internal/external validation set, 1009/112/245) and AI-ID (training/internal/external validation set, 620/68/155) was 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 EUS results, respectively.
Results |
The AI-DDx showed good diagnostic performance for both internal (area under the receiver operating characteristic curve [AUROC] = .86) and external validation (AUROC = .86). The performance of the AI-DDx was better than that of novice (AUROC = .82, P = .01) and intermediate endoscopists (AUROC = .84, P = .02) but was comparable with experts (AUROC = .89, P = .12) in the external validation set. The AI-ID showed a fair performance in both internal (AUROC = .78) and external validation sets (AUROC = .73), which were significantly better than EUS results performed by experts (internal validation, AUROC = .62; external validation, AUROC = .56; both P < .001).
Conclusions |
The AI-DDx was comparable with experts and outperformed novice and intermediate endoscopists for the differential diagnosis of gastric mucosal lesions. The AI-ID performed better than EUS for evaluation of invasion depth.
Le texte complet de cet article est disponible en PDF.Abbreviations : AGC, AI, AI-DDx, AI-ID, AI-LD, AUROC, BGU, CNN, EGC, Grad-AM, ROI
Plan
| DISCLOSURE: All authors disclosed no financial relationships. Research support for this study was provided by Seoul National University Hospital (grant no. 04-2020-0860). |
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| DIVERSITY, EQUITY, AND INCLUSION: We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure ethnic or other types of diversity in the recruitment of human subjects. We worked to ensure that the language of the study questionnaires reflected inclusion. |
Vol 95 - N° 2
P. 258 - février 2022 Retour au numéro
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