Artificial intelligence, machine learning, and deep learning in rhinology: 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: Oct 19, 2022

Abstract

PURPOSE: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability.

Authors

  • Antonio Mario Bulfamante
    Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università Degli Studi di Milano, Milan, Italy.
  • Francesco Ferella
    Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy.
  • Austin Michael Miller
    Ohio University Heritage College of Osteopathic Medicine, Dublin, OH, USA.
  • Cecilia Rosso
    Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy.
  • Carlotta Pipolo
    Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università Degli Studi di Milano, Milan, Italy.
  • Emanuela Fuccillo
    Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy.
  • Giovanni Felisati
    Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università Degli Studi di Milano, Milan, Italy.
  • Alberto Maria Saibene
    Study Group of Young-Otolaryngologists of the International Federations of Oto-rhino-laryngological Societies (YO-IFOS), Paris, France.

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