Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis

Na Li, Jian Yang, Xiaodong Li, Yanting Shi and Kunhong Wang

PLOS ONE, 2024, vol. 19, issue 5, 1-16

Abstract: Background and aims: Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI’s diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists’ ability, and explore the main factors affecting AI’s performance. Methods: The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. Results: Twelve studies with 11,173 patients were included, demonstrating AI models’ efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92–0.96) and specificity of 93% (95% confidence interval: 0.89–0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). Conclusions: AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0303421

DOI: 10.1371/journal.pone.0303421

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