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- W3048487030 endingPage "353" @default.
- W3048487030 startingPage "346" @default.
- W3048487030 abstract "Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for realtime cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:346-353)" @default.
- W3048487030 created "2020-08-18" @default.
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- W3048487030 date "2021-05-15" @default.
- W3048487030 modified "2023-10-17" @default.
- W3048487030 title "Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm" @default.
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- W3048487030 doi "https://doi.org/10.5009/gnl20186" @default.
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