Automatic grading of patients with a unilateral facial paralysis based on the Sunnybrook Facial Grading System - A deep learning study based on a convolutional neural network.

Journal: American journal of otolaryngology
Published Date: Feb 25, 2023

Abstract

PURPOSE: In order to assess the severity and the progression of a unilateral peripheral facial palsy the Sunnybrook Facial Grading System (SFGS) is a well-established grading system due to its clinical relevance, sensitivity, and robust measuring method. However, training is required in order to achieve a high inter-rater reliability. This study investigated the automated grading of facial palsy patients based on the SFGS using a convolutional neural network.

Authors

  • Timen C Ten Harkel
    Radboud University Medical Centre, 3D Lab Radboudumc, Nijmegen 6500 HB, the Netherlands; Radboud University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen 6500 HB, the Netherlands. Electronic address: Timen.tenHarkel@radboudumc.nl.
  • Guido de Jong
    Department of Neurosurgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Henri A M Marres
    Radboud University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen 6500 HB, the Netherlands.
  • Koen J A O Ingels
    Radboud University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen 6500 HB, the Netherlands.
  • Caroline M Speksnijder
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Thomas J J Maal
    Radboud University Medical Centre, 3D Lab Radboudumc, Nijmegen 6500 HB, the Netherlands; Radboud University Medical Centre, Department of Oral and Maxillofacial Surgery, Nijmegen 6500 HB, the Netherlands.

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