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

Sruthi Surapaneni1,2, Rachel B Kutler1,3, Sean A Setzen1,4

  • 1Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College, Sean Parker Institute for the Voice, New York, New York, USA.

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Summary
This summary is machine-generated.

A new deep-learning model combining stroboscopic images and voice data accurately identifies vocal fold (VF) conditions. This multimodal approach shows promise for diagnosing VF paralysis and lesions.

Keywords:
artificial intelligencemultimodaltransformer

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Area of Science:

  • Otolaryngology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Vocal fold (VF) pathologies, including unilateral paralysis (UVFP) and lesions, require accurate diagnosis.
  • Current diagnostic methods may benefit from advanced computational analysis.

Purpose of the Study:

  • To develop and validate a multimodal deep-learning classifier for differentiating between healthy vocal folds (HVF), UVFP, and VF lesions.
  • To compare the performance of a multimodal classifier against unimodal image-only and audio-only classifiers.

Main Methods:

  • Retrospective analysis of patients with UVFP, VF lesions, and HVF.
  • Extraction of image frames and voice samples from stroboscopic videos; collection of clinicodemographic data.
  • Development of a multimodal classifier combining visual, acoustic, and demographic features using deep learning.

Main Results:

  • The multimodal classifier achieved 76.9% accuracy on a test set, outperforming image-only (61.5%) and audio-only (65.4%) models.
  • On an external dataset, the multimodal classifier accuracy was 45%, still superior to unimodal models (42% and 31%).

Conclusions:

  • A multimodal deep-learning approach combining stroboscopic images, voice, and clinical data shows potential for diagnosing vocal fold pathologies.
  • This proof-of-concept study highlights the benefit of integrating diverse data modalities for improved diagnostic accuracy.