Health AI Central
Peer-Reviewed Publication
Laryngoscope2026;136(6):2503-2510.June 1, 2026Journal Article

A Multimodal Approach for Deep-Learning Classification of Vocal Fold Pathologies in Stroboscopy.

Sruthi Surapaneni1,2, Rachel B Kutler1,3, Sean A Setzen1,4, Yeo Eun Kim1, Peter Yao1, Sana H Siddiqui1,3, Michael J Pitman3, Lucian Sulica1, Olivier Elemento5, Pegah Khosravi6,7, Anaïs Rameau1
1Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College, Sean Parker Institute for the Voice, New York, New York, USA.
2Michigan State University College of Human Medicine, Grand Rapids, Michigan, USA.
3The Center for Voice and Swallowing, Department of Otolaryngology-Head and Neck Surgery, Columbia University Irving Medical Center, New York, New York, USA.
4Department of Otolaryngology-Head and Neck Surgery, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA.
5Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA.
6Department of Clinical Sciences, College of Medicine, University of Central Florida, Orlando, Florida, USA.
7Institute for Artificial Intelligence, Department of Computer Science, University of Central Florida, Orlando, Florida, USA.
PubMed PMC DOI PDF

Abstract

OBJECTIVE: To develop and validate a multimodal deep-learning 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. METHODS: Patients with UVFP (n = 54), VF lesions (n = 42), and HVF (n = 41) were retrospect…

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