{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T22:15:05Z","timestamp":1790720105341,"version":"4.1.0"},"reference-count":16,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2019,3,25]],"date-time":"2019-03-25T00:00:00Z","timestamp":1553472000000},"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":["Laryngoscope Investig Oto"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Acoustic analysis of voice has the potential to expedite detection and diagnosis of voice disorders. Applying an image\u2010based, neural\u2010network approach to analyzing the acoustic signal may be an effective means for detecting and differentially diagnosing voice disorders. The purpose of this study is to provide a proof\u2010of\u2010concept that embedded data within human phonation can be accurately and efficiently decoded with deep learning neural network analysis to differentiate between normal and disordered voices.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Acoustic recordings from 10 vocally\u2010healthy speakers, as well as 70 patients with one of seven voice disorders (n\u2009=\u200910 per diagnosis), were acquired from a clinical database. Acoustic signals were converted into spectrograms and used to train a convolutional neural network developed with the Keras library. The network architecture was trained separately for each of the seven diagnostic categories. Binary classification tasks (ie, to classify normal vs. disordered) were performed for each of the seven diagnostic categories. All models were validated using the 10\u2010fold cross\u2010validation technique.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Binary classification averaged accuracies ranged from 58% to 90%. Models were most accurate in their classification of adductor spasmodic dysphonia, unilateral vocal fold paralysis, vocal fold polyp, polypoid corditis, and recurrent respiratory papillomatosis. Despite a small sample size, these findings are consistent with previously published data utilizing deep neural networks for classification of voice disorders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Promising preliminary results support further study of deep neural networks for clinical detection and diagnosis of human voice disorders. Current models should be optimized with a larger sample size.<\/jats:p><\/jats:sec><jats:sec><jats:title>Levels of Evidence<\/jats:title><jats:p>Level III<\/jats:p><\/jats:sec>","DOI":"10.1002\/lio2.259","type":"journal-article","created":{"date-parts":[[2019,3,25]],"date-time":"2019-03-25T19:41:16Z","timestamp":1553542876000},"page":"328-334","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Decoding phonation with artificial intelligence (D<scp>e<\/scp>P AI): Proof of concept"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6643-8991","authenticated-orcid":false,"given":"Maria E.","family":"Powell","sequence":"first","affiliation":[{"name":"Vanderbilt Bill Wilkerson Center for Otolaryngology Vanderbilt University Medical Center  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelino","family":"Rodriguez Cancio","sequence":"additional","affiliation":[{"name":"Department of Information Technology Vanderbilt University  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Young","sequence":"additional","affiliation":[{"name":"Vanderbilt Bill Wilkerson Center for Otolaryngology Vanderbilt University Medical Center  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Nock","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science Vanderbilt University  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beshoy","family":"Abdelmessih","sequence":"additional","affiliation":[{"name":"Vanderbilt Bill Wilkerson Center for Otolaryngology Vanderbilt University Medical Center  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Zeller","sequence":"additional","affiliation":[{"name":"Vanderbilt Bill Wilkerson Center for Otolaryngology Vanderbilt University Medical Center  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irvin","family":"Perez Morales","sequence":"additional","affiliation":[{"name":"Center of Research in Computational and Numerical Methods in Engineering Central University Marta Abreu of Las Villas  Santa Clara Cuba"},{"name":"Infralab University of Bras\u00edlia  Bras\u00edlia Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science Vanderbilt University  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. Gaelyn","family":"Garrett","sequence":"additional","affiliation":[{"name":"Vanderbilt Bill Wilkerson Center for Otolaryngology Vanderbilt University Medical Center  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science Vanderbilt University  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules","family":"White","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science Vanderbilt University  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Gelbard","sequence":"additional","affiliation":[{"name":"Vanderbilt Bill Wilkerson Center for Otolaryngology Vanderbilt University Medical Center  Nashville Tennessee U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,3,25]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1044\/jshr.3301.103"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1121\/1.1918465"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2006.06.003"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1350-4533(02)00031-0"},{"issue":"4","key":"e_1_2_6_6_1","first-page":"270","article-title":"Novel approach to acoustical voice analysis using artificial neural networks","volume":"1","author":"Sch\u00f6nweiler R","year":"2000","journal-title":"J Assoc Res Otolaryngol"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2006.09.003"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2003.820386"},{"key":"e_1_2_6_9_1","first-page":"1096","volume-title":"Advances in Neural Information Processing Systems","author":"Lee H","year":"2009"},{"key":"e_1_2_6_10_1","first-page":"8604","article-title":"Recent advances in deep learning for speech research at Microsoft","volume":"2013","author":"Deng L","year":"2013","journal-title":"IEEE Int Conf Acoust Speech Signal Process"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2014.01.010"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_6_13_1","volume-title":"Voice and Articulation Drillbook","author":"Fairbanks G.","year":"1960"},{"key":"e_1_2_6_14_1","first-page":"547","article-title":"Audio based bird species identification using deep learning techniques","author":"Sprengel E","year":"2016","journal-title":"LifeCLEF"},{"key":"e_1_2_6_15_1","volume-title":"Deep Learning with Python","author":"Chollet F.","year":"2017"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2007.12.004"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2657381"}],"container-title":["Laryngoscope Investigative Otolaryngology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/lio2.259","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/lio2.259","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/lio2.259","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T14:42:49Z","timestamp":1694184169000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/lio2.259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,25]]},"references-count":16,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["10.1002\/lio2.259"],"URL":"https:\/\/doi.org\/10.1002\/lio2.259","archive":["Portico"],"relation":{},"ISSN":["2378-8038","2378-8038"],"issn-type":[{"value":"2378-8038","type":"print"},{"value":"2378-8038","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,25]]},"assertion":[{"value":"2018-11-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-03-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}