{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:32:44Z","timestamp":1781047964980,"version":"3.54.1"},"reference-count":22,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:sec><jats:title>Objectives\/Hypothesis<\/jats:title><jats:p>There may be an interobserver variation in the diagnosis of laryngeal disease based on laryngoscopic images according to clinical experience. Therefore, this study is aimed to perform computer\u2010assisted diagnosis for common laryngeal diseases using deep learning\u2010based disease classification models.<\/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>A total of 4106 images (cysts, nodules, polyps, leukoplakia, papillomas, Reinke's edema, granulomas, palsies, and normal cases) were analyzed. After equal distribution of diseases into ninefolds, stratified eightfold cross\u2010validation was performed for training, validation process and remaining onefold was used as a test dataset. A trained model was applied to test sets, and model performance was assessed for precision (positive predictive value), recall (sensitivity), accuracy, F1 score, precision\u2013recall (PR) curve, and PR\u2010area under the receiver operating characteristic curve (PR\u2010AUC). Outcomes were compared to those of visual assessments by four trainees.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The trained deep neural networks (DNNs) outperformed trainees' visual assessments in discriminating cysts, granulomas, nodules, normal cases, palsies, papillomas, and polyps according to the PR\u2010AUC and F1 score. The lowest F1 score and PR\u2010AUC of DNNs were estimated for Reinke's edema (0.720, 0.800) and nodules (0.730, 0.780) but were comparable to the mean of the two trainees' F1 score with the best performances (0.765 and 0.675, respectively). In discriminating papillomas, the F1 score was much higher for DNNs (0.870) than for trainees (0.685). Overall, DNNs outperformed all trainees (micro\u2010average PR\u2010AUC\u00a0=\u00a00.95; macro\u2010average PR\u2010AUC\u00a0=\u00a00.91).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>DNN technology could be applied to laryngoscopy to supplement clinical assessment of examiners by providing additional diagnostic clues and having a role as a reference of diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3 <jats:italic>Laryngoscope<\/jats:italic>, 131:2558\u20132566, 2021<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.29595","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T17:30:51Z","timestamp":1621272651000},"page":"2558-2566","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network\u2010Based Image Classification System"],"prefix":"10.1002","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7577-2214","authenticated-orcid":false,"given":"Won Ki","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeong Ju","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2830-2145","authenticated-orcid":false,"given":"Hye Ah","family":"Joo","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"In Seong","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6990-2454","authenticated-orcid":false,"given":"Yeonjoo","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soon Yuhl","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Yoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung\u2010Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology\u2013Head and Neck Surgery, Asan Medical Center University of Ulsan College of Medicine  Seoul Republic of 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