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.

Participants with unilateral Bell's palsy will be recruited for a single assessment session. The assessment involves two primary components:

Clinical Assessment: A qualified physical therapist will grade the patient's facial function using the Sunnybrook Facial Grading System, which evaluates resting symmetry, degree of voluntary movement, and synkinesis.

AI Assessment: A computer-vision-based system will utilize a standard camera to detect 468 facial landmarks in real-time. The system calculates a composite asymmetry score by comparing the movement amplitude and positioning of the affected side of the face against the healthy side during standardized facial expressions.

The study will utilize Spearman's rank correlation coefficient to analyze the relationship between the AI-derived scores and the Sunnybrook scores to establish concurrent validity. No personal images or videos will be stored; the AI performs real-time processing and immediate data deletion to ensure participant privacy.

Inclusion Criteria:

• Patients with unilateral Bell's palsy.
• Patients must be within one month of onset of Bell's palsy symptoms at the time of enrollment.
• Body mass index (BMI) less than 30 $kg/m^2$.
• Patients must be cooperative and able to follow simple verbal instructions during facial movement tasks.

Exclusion Criteria:

• Bilateral facial paralysis or recurrent Bell's palsy.
• Facial nerve palsy due to known secondary causes (e.g., trauma, neoplasm, infection, stroke, Ramsay Hunt syndrome, or otitis media).
• Facial deformities, scars, or burns that interfere with facial motion detection.
• Uncooperative or cognitively impaired individuals unable to follow instructions or maintain required facial postures.

Sixty-three patients of both sexes with unilateral Bell's palsy will be recruited from private clinics. Patients will be diagnosed by a neurologist based on clinical examination.

To maintain participant confidentiality and privacy. The AI system processes facial landmarks in real-time, and no individual images or raw biometric data are stored for future distribution