  Importance: Machine learning (ML) approaches to facial landmark localization
carry great clinical potential for quantitative assessment of facial function
as they enable high-throughput automated quantification of relevant facial
metrics from photographs. However, translation from research settings to
clinical applications requires important improvements. Objective: To develop an
ML algorithm for accurate facial landmarks localization in photographs of
facial palsy patients, and use it as part of an automated computer-aided
diagnosis system. Design, Setting, and Participants: Facial landmarks were
manually localized in portrait photographs of eight expressions obtained from
200 facial palsy patients and 10 controls. A novel ML model for automated
facial landmark localization was trained using this disease-specific database.
Model output was compared to manual annotations and the output of a model
trained using a larger database consisting only of healthy subjects. Model
accuracy was evaluated by the normalized root mean square error (NRMSE) between
algorithms' prediction and manual annotations. Results: Publicly available
algorithms provide poor results when applied to patients compared to healthy
controls (NRMSE, 8.56 +/- 2.16 vs. 7.09 +/- 2.34, p << 0.01). We found
significant improvement in facial landmark localization accuracy for the
clinical population when using a model trained with a relatively small number
patients' photographs (1440) compared to a model trained using several thousand
more images of healthy faces (NRMSE, 6.03 +/- 2.43 vs. 8.56 +/- 2.16, p <<
0.01). Conclusions: Retraining a landmark detection model with a small number
of clinical images significantly improved landmark detection performance in
frontal view photographs of the clinical population. These results represent
the first steps towards an automatic system for computer-aided assessment in
facial palsy.
