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

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). METHODS: The Massachusetts Eye and Ear Infirmary Standard Facial Palsy Dataset was employed. Clinician-graded eFACE assessment was performed on 160 photographs. A Python script was used to automatically generate auto-eFACE scores on the same photographs. eFACE and auto-eFACE scores were compared for normal, flaccidly paralyzed, and synkinetic faces. RESULTS: Auto-eFACE and eFACE scores differentiated normal faces from those with facial palsy. Auto-eFACE produced significantly lower scores than eFACE for normal faces (93.83 ± 4.37 versus 100.00 ± 1.58; p = 0.01). Review of photographs revealed minor facial asymmetries in normal faces that clinicians tend to disregard. Auto-eFACE reported better facial symmetry in patients with flaccid paralysis (59.96 ± 5.80) and severe synkinesis (62.35 ± 9.35) than clinician-graded eFACE (52.20 ± 3.39 and 54.22 ± 5.35, respectively; p = 0.080 and p = 0.080, respectively); this result trended toward significance. CONCLUSIONS: Auto-eFACE scores can be obtained automatically using a freely available machine learning-based computer software. Automated scores predicted more asymmetry in normal patients, and less asymmetry in patients with flaccid palsy and synkinesis, compared to clinician grading. Auto-eFACE is a quick and easy-to-use assessment tool that holds promise for standardization of facial palsy outcome measures and may eliminate observer bias seen in clinician-graded scales. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, III.

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