Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Published Date: Dec 13, 2023

Abstract

OBJECTIVES: With smartphones and wearable devices becoming ubiquitous, they offer an opportunity for large-scale voice sampling. This systematic review explores the application of deep learning models for the automated analysis of voice samples to detect vocal cord pathologies.

Authors

  • Idit Tessler
    Department of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel. idit.tessler@gmail.com.
  • Adi Primov-Fever
    Department of Otolaryngology Head and Neck Surgery, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
  • Shelly Soffer
    From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.).
  • Roi Anteby
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel. roianteby@mail.tau.ac.il.
  • Nir A Gecel
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Nir Livneh
    Head and Neck Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • Eran E Alon
    Department of Otolaryngology, Sheba Medical Center, Ramat Gan, Israel.
  • Eyal Zimlichman
    Sheba Medical Center, Tel Hashomer, Israel.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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