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The Auto-eFACE: Machine Learning-Enhanced Program Yields Automated Facial Palsy Assessment Tool
Matthew Q Miller1, Tessa A Hadlock1, Emily Fortier1
1From the Massachusetts Eye and Ear Infirmary, Harvard Medical School; and the Biomedical Engineering Program, Florida Institute of Technology.
Plastic and Reconstructive Surgery
|November 25, 2020
Summary
Automated facial palsy assessment (auto-eFACE) shows promise for standardizing evaluations and reducing observer bias. This machine learning tool offers a quick and easy method for objective facial palsy grading, improving treatment comparisons.
Area of Science:
- Medical imaging analysis
- Machine learning in healthcare
- Facial nerve disorder diagnostics
Background:
- Facial palsy assessment lacks standardization, relying on subjective clinician grading.
- Existing clinician-graded scales are prone to subjectivity and observer bias.
- Objective, computer-aided grading is needed for consistent facial palsy assessment and treatment efficacy comparison.
Purpose of the Study:
- To compare the clinician-graded eFACE scale with a machine learning-derived automated assessment (auto-eFACE).
- To evaluate the reliability and objectivity of automated facial palsy grading.
- To determine if auto-eFACE can provide more consistent and accurate facial symmetry measurements.
Main Methods:
- Utilized the Massachusetts Eye and Ear Infirmary Standard Facial Palsy Dataset (160 photographs).
- Performed clinician-graded eFACE assessments on all photographs.
- Developed a Python script to automatically generate auto-eFACE scores for comparison.
Main Results:
- Both auto-eFACE and eFACE differentiated normal from facial palsy cases.
- Auto-eFACE reported significantly lower scores for normal faces, indicating detection of subtle asymmetries.
- Auto-eFACE showed a trend towards better symmetry detection in flaccid paralysis and synkinesis compared to eFACE.
Conclusions:
- Auto-eFACE, a machine learning tool, provides automated facial palsy scores.
- Automated grading demonstrated higher asymmetry detection in normal faces and lower in affected faces versus clinician grading.
- Auto-eFACE offers a promising, objective, and standardized approach to facial palsy outcome measurement, reducing observer bias.
