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• W3131569496 abstract "Machine learning (ML) algorithms have augmented human judgment in various fields of clinical medicine. However, little progress has been made in applying these tools to video-endoscopy. We reviewed the field of video-analysis (herein termed 'Videomics' for the first time) as applied to diagnostic endoscopy, assessing its preliminary findings, potential, as well as limitations, and consider future developments.ML has been applied to diagnostic endoscopy with different aims: blind-spot detection, automatic quality control, lesion detection, classification, and characterization. The early experience in gastrointestinal endoscopy has recently been expanded to the upper aerodigestive tract, demonstrating promising results in both clinical fields. From top to bottom, multispectral imaging (such as Narrow Band Imaging) appeared to provide significant information drawn from endoscopic images.Videomics is an emerging discipline that has the potential to significantly improve human detection and characterization of clinically significant lesions during endoscopy across medical and surgical disciplines. Research teams should focus on the standardization of data collection, identification of common targets, and optimal reporting. With such a collaborative stepwise approach, Videomics is likely to soon augment clinical endoscopy, significantly impacting cancer patient outcomes." @default.
• W3131569496 created "2021-03-01" @default.
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• W3131569496 date "2021-02-11" @default.
• W3131569496 modified "2023-10-18" @default.
• W3131569496 title "Videomics: bringing deep learning to diagnostic endoscopy" @default.
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• W3131569496 doi "https://doi.org/10.1097/moo.0000000000000697" @default.
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