The Auto-eFACE: Machine Learning-Enhanced Program Yields Automated Facial Palsy Assessment Tool.

Journal: Plastic and reconstructive surgery
PMID: 33235050

Abstract

BACKGROUND: Facial palsy assessment is nonstandardized. Clinician-graded scales are limited by subjectivity and observer bias. Computer-aided grading would be desirable to achieve conformity in facial palsy assessment and to compare the effectiveness of treatments. This research compares the clinician-graded eFACE scale to machine learning-derived automated assessments (auto-eFACE).

Authors

  • Matthew Q Miller
    From the Massachusetts Eye and Ear Infirmary, Harvard Medical School; and the Biomedical Engineering Program, Florida Institute of Technology.
  • Tessa A Hadlock
    Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Cambridge.
  • Emily Fortier
    Department of Otolaryngology/Head and Neck Surgery, Massachusetts Eye and Ear Infirmary and Harvard Medical School, Boston, Massachusetts.
  • Diego L Guarin
    Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Cambridge.

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