mdphd.weill.cornell.edu/about-program/our-students/student-publications/deep-learning-model-multi-class-audio-classification-vocal-fold-pathologies-office-stroboscopy

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      A Deep-Learning Model for Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy. | Tri-Institutional MD-PhD Program
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      OBJECTIVE: To develop and validate a deep-learning classifier trained on voice data extracted from videolaryngostroboscopy recordings, 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 = 105), VF lesions (n = 63), and HVF (n = 41)
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