{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T06:02:49Z","timestamp":1782021769799,"version":"3.54.5"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a multimodal deep\u2010learning classifier trained on stroboscopic image, voice, and clinicodemographic data, differentiating between three different vocal fold (VF) states: healthy (HVF), unilateral paralysis (UVFP), and VF lesions, including benign and malignant pathologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patients with UVFP (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200954), VF lesions (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200942), and HVF (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200941) were retrospectively identified. Image frames and voice samples were extracted from stroboscopic videos. Clinicodemographic variables were collected from the electronic health record. Patient\u2010level data was independently divided into training (80%) and testing (20%). Visual features were extracted using a transformer DINOv2 and acoustic features were extracted using Librosa. All three feature modalities were combined using a custom multilayer perceptron. Unimodality models using only image or only voice data were trained for comparison. Accuracy and\n                      <jats:italic>F<\/jats:italic>\n                      1 scores were used to validate the models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On a hold\u2010out test set, the multimodal classifier demonstrated stronger performance (76.9% accuracy) compared to the image classifier (61.5% accuracy) and audio classifier (65.4% accuracy). On an external dataset, the multimodal classifier accuracy dropped to 45%, though still an improvement compared to accuracies of 42% and 31% for the video\u2010only and audio\u2010only modalities, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this proof\u2010of\u2010concept study, we successfully developed a multimodal dataset and classifier for VF pathology, demonstrating the potential of combining stroboscopic frames, voice and text data. The multimodal classifier achieved higher accuracy than the image\u2010only model and audio\u2010only models. Future models should validate these findings on larger datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>4<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/lary.70355","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:16:21Z","timestamp":1767611781000},"page":"2503-2510","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multimodal Approach for Deep\u2010Learning Classification of Vocal Fold Pathologies in Stroboscopy"],"prefix":"10.1002","volume":"136","author":[{"given":"Sruthi","family":"Surapaneni","sequence":"first","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Weill Cornell Medical College, Sean Parker Institute for the Voice  New York New York USA"},{"name":"Michigan State University College of Human Medicine  Grand Rapids Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5202-6888","authenticated-orcid":false,"given":"Rachel B.","family":"Kutler","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Weill Cornell Medical College, Sean Parker Institute for the Voice  New York New York USA"},{"name":"The Center for Voice and Swallowing, Department of Otolaryngology\u2010Head and Neck Surgery Columbia University Irving Medical Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5796-4527","authenticated-orcid":false,"given":"Sean A.","family":"Setzen","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Weill Cornell Medical College, Sean Parker Institute for the Voice  New York New York USA"},{"name":"Department of Otolaryngology\u2010Head and Neck Surgery St. Luke's University Health Network  Bethlehem Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7201-7545","authenticated-orcid":false,"given":"Yeo Eun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Weill Cornell Medical College, Sean Parker Institute for the Voice  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Weill Cornell Medical College, Sean Parker Institute for the Voice  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sana H.","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology\u2010Head and Neck Surgery Weill Cornell Medical College, Sean Parker Institute for the Voice  New York New York 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