Predicting Therapeutic Response to Hypoglossal Nerve Stimulation Using Deep Learning.

Journal: The Laryngoscope
PMID: 38934474

Abstract

OBJECTIVES: To develop and validate machine learning (ML) and deep learning (DL) models using drug-induced sleep endoscopy (DISE) images to predict the therapeutic efficacy of hypoglossal nerve stimulator (HGNS) implantation.

Authors

  • Rahul Alapati
    Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania.
  • Bryan Renslo
    Department of Otolaryngology-Head and Neck Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A.
  • Laura Jackson
    University of Kansas School of Medicine, Kansas City, Kansas, U.S.A.
  • Hanna Moradi
    University of Kansas School of Medicine, Kansas City, Kansas, U.S.A.
  • Jamie R Oliver
    Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA.
  • Mohsena Chowdhury
    Toronto Metropolitan University, Toronto, Ontario, Canada.
  • Tejas Vyas
    Toronto Metropolitan University, Toronto, Ontario, Canada.
  • Antonio Bon Nieves
    Department of Otolaryngology-Head and Neck Surgery, University of Kansas, Kansas City, Kansas, U.S.A.
  • Amelia Lawrence
    Department of Otolaryngology-Head and Neck Surgery, University of Kansas, Kansas City, Kansas, U.S.A.
  • Sarah F Wagoner
    Department of Otolaryngology-Head and Neck Surgery, University of Kansas, Kansas City, Kansas, U.S.A.
  • David Rouse
    Department of Otolaryngology-Head and Neck Surgery, University of Kansas, Kansas City, Kansas, U.S.A.
  • Christopher G Larsen
    Department of Otolaryngology-Head and Neck Surgery, University of Kansas, Kansas City, Kansas, U.S.A.
  • Ganghui Wang
    Toronto Metropolitan University, Toronto, Ontario, Canada.
  • AndrĂ©s M Bur
    1 Department of Otolaryngology-Head and Neck Surgery, School of Medicine, University of Kansas, Kansas City, Kansas, USA.

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