{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:14:18Z","timestamp":1790108058860,"version":"4.0.1"},"reference-count":46,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T00:00:00Z","timestamp":1718150400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records can detect glottic neoplasm from benign voice disorders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a primary dataset containing 2\u20133\u2009s of vowel \u201cah\u201d, demographics, and 26 items of structured medical records (e.g., symptoms, comorbidity, smoking and alcohol consumption, vocal demand) from 60 patients with pathology\u2010proved glottic neoplasm (i.e., squamous cell carcinoma, carcinoma in situ, and dysplasia) and 1940 patients with benign voice disorders. The validation dataset comprised data from 23 patients with glottic neoplasm and 1331 patients with benign disorders. The AI model combined convolutional neural networks, gated recurrent units, and attention layers. We used 10\u2010fold cross\u2010validation (training\u2013validation\u2013testing: 8\u20131\u20131) and preserved the percentage between neoplasm and benign disorders in each fold.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results from the AI model using voice signals reached an area under the ROC curve (AUC) value of 0.631, and additional demographics increased this to 0.807. The highest AUC of 0.878 was achieved when combining voice, demographics, and medical records (sensitivity: 0.783, specificity: 0.816, accuracy: 0.815). External validation yielded an AUC value of 0.785 (voice plus demographics; sensitivity: 0.739, specificity: 0.745, accuracy: 0.745). Subanalysis showed that AI had higher sensitivity but lower specificity than human assessment (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01). The accuracy of AI detection with additional medical records was comparable with human assessment (82% vs. 83%, <jats:italic>p<\/jats:italic>\u2009=\u20090.78).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Voice signal alone was insufficient for AI differentiation between glottic neoplasm and benign voice disorders, but additional demographics and medical records notably improved AI performance and approximated the prediction accuracy of humans.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>NA <jats:italic>Laryngoscope<\/jats:italic>, 134:4585\u20134592, 2024<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.31563","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T09:30:04Z","timestamp":1718184604000},"page":"4585-4592","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["<scp>AI<\/scp> Detection of Glottic Neoplasm Using Voice Signals, Demographics, and Structured Medical Records"],"prefix":"10.1002","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-1291","authenticated-orcid":false,"given":"Chi\u2010Te","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Otolaryngology Head and Neck Surgery Far Eastern Memorial Hospital  Taipei Taiwan"},{"name":"Center of Artificial Intelligence, Far Eastern Memorial Hospital  Taipei Taiwan"},{"name":"Department of Electrical Engineering Yuan Ze University  Taoyuan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsai\u2010Min","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate Program of Data Science, National Taiwan University and Academia Sinica  Taipei Taiwan"},{"name":"Research Center for Information Technology Innovation, Academia Sinica  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nien\u2010Ting","family":"Lee","sequence":"additional","affiliation":[{"name":"Center of Artificial Intelligence, Far Eastern Memorial Hospital  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih\u2010Hau","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Yuan Ze University  Taoyuan Taiwan"},{"name":"Department of Electrical Engineering National Taiwan Normal University  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.metabol.2017.01.011"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2020.104885"},{"key":"e_1_2_7_4_1","unstructured":"MahmoodR BabierA McNivenA DiamantA ChanTC.Automated treatment planning in radiation therapy using generative adversarial networks. 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