Artificial Intelligence in Facial Palsy Treatment: A Systematic Review and Recommendations

Artificial intelligence (AI) is rapidly advancing and increasingly applied in facial palsy research. However, there is no comprehensive review to guide surgeons on AI-based facial assessment tools. Although photographic standards exist, videographic standards for emotions have not been proposed. Implementing these standards is essential for improving information exchange and data comparison with the new AI tools.

The authors conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, by analyzing databases including MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. The authors' focus was on the use of AI-based facial assessment tools in patients with facial palsy who subsequently received intervention or surgery for their facial palsy. Data were evaluated descriptively, and recommendations, including videographic standards, were developed in collaboration with experts from multiple centers.

The authors identified 3222 articles, 35 of which met the inclusion criteria. Five AI applications analyzed static, dynamic, and chemodenervation procedures in unilateral or bilateral facial palsy. These focused on specific facial landmarks or emotion recognition from photographs and videos, but varied in the expressions and emotions analyzed. Five studies provided validation data with either healthy subjects or other outcome measurements. The authors recommend a minimum videographic assessment including the validated emotions neutral and happy.

AI-related publications on facial palsy have significantly increased, but no consensus exists on the optimal AI-assessment software. The proposed flowchart from the authors' systematic review can guide clinicians in decision-making. The authors recommend using the proposed videographic emotions to improve study consistency and comparability, and also encouraging further validation studies.