Validation of Artificial Intelligence-Based Facial Paralysis Assessment in Patients With Bell's Palsy

Condition(s) studied

Investigational drug(s) / intervention(s)

Sunnybrook Facial Grading System (FGS): Clinical grading of facial muscle paralysis based on resting symmetry, symmetry of voluntary movements, and synkinesis detection.

AI-Based Facial Assessment: Real-time computer vision analysis using deep-learning-based landmark detection to track 468 facial points during standardized facial expressions.

Study summary

This observational study aims to assess the concurrent validity of an artificial intelligence (AI)-based facial paralysis assessment system in patients with unilateral Bell's palsy. Currently, clinical assessment relies on subjective scales like the Sunnybrook Facial Grading System, which can vary between different observers. This study will compare AI-generated composite asymmetry scores-derived from real-time computer vision analysis of facial landmarks-with scores from the Sunnybrook system. The goal is to determine if AI can provide a valid, objective method for monitoring facial nerve recovery.

Eligibility

Primary outcome measure(s)

• Spearman's Rank Correlation Coefficient between AI-derived scores and Sunnybrook Facial Grading System scores. — Baseline (single assessment at the time of enrollment).
  This measure evaluates the concurrent validity of the AI-based assessment system. The AI system uses 468 facial landmarks to calculate a composite asymmetry score (0-100%). These results will be correlated with the clinical scores from the Sunnybrook Facial Grading System (0-100), where higher scores indicate better facial function.

Trial sites (1)

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