Frontiers in medicine · 2023
Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis.
Систематический обзорСтатьяНазвание и аннотация: Перевод в очереди
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Shi Yanting, Wei Ning, Wang Kunhong, Tao Tao, Yu Feng, Lv Bing
Аннотация
BACKGROUND AND AIMS: The diagnosis of chronic atrophic gastritis (CAG) under normal white-light endoscopy depends on the endoscopist's experience and is not ideal. Artificial intelligence (AI) is increasingly used to diagnose diseases with good results. This review aimed to evaluate the accuracy of AI-assisted diagnosis of CAG through a meta-analysis.
METHODS: We conducted a comprehensive literature search of four databases: PubMed, Embase, Web of Science, and the Cochrane Library. Studies published by November 21, 2022, on AI diagnosis CAG with endoscopic images or videos were included. We assessed the diagnostic performance of AI using meta-analysis, explored the sources of heterogeneity through subgroup analysis and meta-regression, and compared the accuracy of AI and endoscopists in diagnosing CAG.
RESULTS: Eight studies that included a total of 25,216 patients of interest, 84,678 image training set images, and 10,937 test set images/videos were included. The results of the meta-analysis showed that the sensitivity of AI in identifying CAG was 94% (95% confidence interval [CI]: 0.88-0.97, I2 = 96.2%), the specificity was 96% (95% CI: 0.88-0.98, I2 = 98.04%), and the area under the summary receiver operating characteristic curve was 0.98 (95% CI: 0.96-0.99). The accuracy of AI in diagnosing CAG was significantly higher than that of endoscopists.
CONCLUSIONS: AI-assisted diagnosis of CAG in endoscopy has high accuracy and clinical diagnostic value.
SYSTEMATIC REVIEW REGISTRATION: http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023391853.
METHODS: We conducted a comprehensive literature search of four databases: PubMed, Embase, Web of Science, and the Cochrane Library. Studies published by November 21, 2022, on AI diagnosis CAG with endoscopic images or videos were included. We assessed the diagnostic performance of AI using meta-analysis, explored the sources of heterogeneity through subgroup analysis and meta-regression, and compared the accuracy of AI and endoscopists in diagnosing CAG.
RESULTS: Eight studies that included a total of 25,216 patients of interest, 84,678 image training set images, and 10,937 test set images/videos were included. The results of the meta-analysis showed that the sensitivity of AI in identifying CAG was 94% (95% confidence interval [CI]: 0.88-0.97, I2 = 96.2%), the specificity was 96% (95% CI: 0.88-0.98, I2 = 98.04%), and the area under the summary receiver operating characteristic curve was 0.98 (95% CI: 0.96-0.99). The accuracy of AI in diagnosing CAG was significantly higher than that of endoscopists.
CONCLUSIONS: AI-assisted diagnosis of CAG in endoscopy has high accuracy and clinical diagnostic value.
SYSTEMATIC REVIEW REGISTRATION: http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023391853.