Endoscopy international open · 2025
Impact of interaction between an artificial intelligence endoscopic support system and endoscopists on diagnosis of gastric neoplastic lesions.
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Mizutani Hiroya, Tsuji Yosuke, Kubota Dai, Hisada Hiroyuki, Miura Yuko, Ohki Daisuke, Takeuchi Chihiro, Kakushima Naomi, Yamamichi Nobutake, Kikuchi Ryosuke, Ishioka Mitsuaki, Yamada Atsuo, Kodashima Shinya, Tada Tomohiro, Fujishiro Mitsuhiro
Аннотация
BACKGROUND AND STUDY AIMS: Artificial intelligence (AI) is expected to enhance the ability of endoscopists to detect gastric neoplastic lesions; however, its effectiveness among highly skilled Japanese expert endoscopists has not been validated. We developed a novel AI-assisted diagnostic tool for detection of gastric neoplastic lesions and evaluated its utility by comparing the diagnostic performance of endoscopists with and without AI assistance.
PATIENTS AND METHODS: Diagnostic performance of gastric neoplastic lesions without and with AI assistance was compared among 14 expert endoscopists and 12 non-expert endoscopists using an evaluation dataset consisting of 150 images containing neoplastic lesions and 350 images without lesions. A general linear mixed model was applied for comparative analysis. The primary outcome was to demonstrate superiority of sensitivity and non-inferiority of specificity among expert endoscopists using AI compared with those without AI. The significance level for sensitivity was set at 2.5% and the non-inferiority margin for specificity was defined as a log odds ratio of -0.25.
RESULTS: Our AI demonstrated superiority in sensitivity (66.4% without AI vs. 83.5% with AI; odds ratio [OR] 2.562, 97.5% confidence interval [CI] 2.069-3.172) and non-inferiority in specificity (90.8% without AI vs. 92.9% with AI; OR 1.326, 95% CI 1.122-1.565) among expert endoscopists.
CONCLUSIONS: AI contributed to improved diagnostic performance even among Japanese expert endoscopists in detecting gastric neoplastic lesions. These findings suggest that the AI system may have potential to support consistently high diagnostic performance across varying levels of endoscopic expertise.
PATIENTS AND METHODS: Diagnostic performance of gastric neoplastic lesions without and with AI assistance was compared among 14 expert endoscopists and 12 non-expert endoscopists using an evaluation dataset consisting of 150 images containing neoplastic lesions and 350 images without lesions. A general linear mixed model was applied for comparative analysis. The primary outcome was to demonstrate superiority of sensitivity and non-inferiority of specificity among expert endoscopists using AI compared with those without AI. The significance level for sensitivity was set at 2.5% and the non-inferiority margin for specificity was defined as a log odds ratio of -0.25.
RESULTS: Our AI demonstrated superiority in sensitivity (66.4% without AI vs. 83.5% with AI; odds ratio [OR] 2.562, 97.5% confidence interval [CI] 2.069-3.172) and non-inferiority in specificity (90.8% without AI vs. 92.9% with AI; OR 1.326, 95% CI 1.122-1.565) among expert endoscopists.
CONCLUSIONS: AI contributed to improved diagnostic performance even among Japanese expert endoscopists in detecting gastric neoplastic lesions. These findings suggest that the AI system may have potential to support consistently high diagnostic performance across varying levels of endoscopic expertise.