Abstract
Automated evaluation of facial palsy using machine learning offers a promising solution
to the limitations of current assessment methods, which can be time-consuming, labor-intensive,
and subject to clinician bias. Deep learning-driven systems have the potential to
rapidly triage patients with varying levels of palsy severity and accurately track
recovery over time. However, developing a clinically usable tool faces several challenges,
such as data quality, inherent biases in machine learning algorithms, and explainability
of decision-making processes. The development of the eFACE scale and its associated
software has improved clinician scoring of facial palsy. Additionally, Emotrics is
a semiautomated tool that provides quantitative data of facial landmarks on patient
photographs. The ideal artificial intelligence (AI)-enabled system would analyze patient
videos in real time, extracting anatomic landmark data to quantify symmetry and movement,
and estimate clinical eFACE scores. This would not replace clinician eFACE scoring
but would offer a rapid automated estimate of both anatomic data, similar to Emotrics,
and clinical severity, similar to the eFACE. This review explores the current state
of facial palsy assessment, recent advancements in AI, and the opportunities and challenges
in developing an AI-driven solution.
Keywords
facial palsy - eFACE - computer vision - artificial intelligence - facial paralysis
