Optimization of the automated Sunnybrook Facial Grading System - Improving the reliability of a deep learning network with facial landmarks.

Journal: European annals of otorhinolaryngology, head and neck diseases
Published Date: Aug 7, 2024

Abstract

OBJECTIVE: The Sunnybrook Facial Grading System (SFGS) is a well-established grading system to assess the severity and progression of a unilateral facial palsy. The automation of the SFGS makes the SFGS more accessible for researchers, students, clinicians in training, or other untrained co-workers and could be implemented in an eHealth environment. This study investigated the impact on the reliability of the automated SFGS by adding a facial landmark layer in a previously developed convolutional neural network (CNN).

Authors

  • T C Ten Harkel
    Radboudumc, 3D Lab Radboudumc, Nijmegen, 6500 HB, The Netherlands; Radboudumc, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen, 6500 HB, The Netherlands. Electronic address: Timen.tenHarkel@radboudumc.nl.
  • F Bielevelt
    Radboudumc, 3D Lab Radboudumc, Nijmegen, 6500 HB, The Netherlands.
  • H A M Marres
    Radboudumc, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen, 6500 HB, The Netherlands.
  • K J A O Ingels
    Radboudumc, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen, 6500 HB, The Netherlands.
  • T J J Maal
    Radboudumc, 3D Lab Radboudumc, Nijmegen, 6500 HB, The Netherlands; Radboudumc, Department of Oral and Maxillofacial Surgery, Nijmegen, 6500 HB, The Netherlands.
  • C M Speksnijder
    Radboudumc, Department of Oral and Maxillofacial Surgery, Nijmegen, 6500 HB, The Netherlands; University Medical Center Utrecht, Utrecht University, Department of Oral and Maxillofacial Surgery, Utrecht, 3508 GA, The Netherlands.

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