{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:54:25Z","timestamp":1791446065701,"version":"4.3.3"},"reference-count":27,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>To assess a new application of artificial intelligence for real\u2010time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow\u2010band imaging (NBI) videolaryngoscopies based on the You\u2010Only\u2010Look\u2010Once (YOLO) deep learning convolutional neural network (CNN).<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Design<\/jats:title><jats:p>Experimental study with retrospective data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Recorded videos of LSCC were retrospectively collected from in\u2010office transnasal videoendoscopies and intraoperative rigid endoscopies. LSCC videoframes were extracted for training, validation, and testing of various YOLO models. Different techniques were used to enhance the image analysis: contrast limited adaptive histogram equalization, data augmentation techniques, and test time augmentation (TTA). The best\u2010performing model was used to assess the automatic detection of LSCC in six videolaryngoscopies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Two hundred and nineteen patients were retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The YOLO models were trained after random distribution of images into a training set (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, the ensemble algorithm (YOLOv5s with YOLOv5m\u2014TTA) achieved the best LSCC detection results, with performance metrics in par with the results reported by other state\u2010of\u2010the\u2010art detection models: 0.66 Precision (positive predicted value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 intersection over union. Tests on the six videolaryngoscopies demonstrated an average computation time per videoframe of 0.026\u00a0seconds. Three demonstration videos are provided.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study identified a suitable CNN model for LSCC detection in WL and NBI videolaryngoscopies. Detection performances are highly promising. The limited complexity and quick computational times for LSCC detection make this model ideal for real\u2010time processing.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3 <jats:italic>Laryngoscope<\/jats:italic>, 132:1798\u20131806, 2022<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.29960","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T09:44:04Z","timestamp":1637919844000},"page":"1798-1806","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real\u2010Time Laryngeal Cancer Detection"],"prefix":"10.1002","volume":"132","author":[{"given":"Muhammad Adeel","family":"Azam","sequence":"first","affiliation":[{"name":"Department of Advanced Robotics Istituto Italiano di Tecnologia  Genoa Italy"},{"name":"Department of Informatics, Bioengineering, Robotics, and 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