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Validation of Artificial Intelligence-Based Facial Paralysis Assessment in Patients With Bell's Palsy
Sponsor: Cairo University
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.
Key Details
Gender
All
Age Range
25 Years - 40 Years
Study Type
OBSERVATIONAL
Enrollment
63
Start Date
2026-06-01
Completion Date
2026-12-01
Last Updated
2026-05-07
Healthy Volunteers
No
Conditions
Interventions
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.
Locations (1)
Faculty of Physical Therapy, Cairo University
Giza, Giza Governorate, Egypt