Bell's Palsy, Facial Nerve Paralysis
Conditions
Keywords
Facial Asymmetry, Artificial Intelligence, Sunnybrook Facial Grading System, Computer Vision
Brief 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.
Detailed description
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.
Interventions
OTHERSunnybrook Facial Grading System (FGS)
Clinical grading of facial muscle paralysis based on resting symmetry, symmetry of voluntary movements, and synkinesis detection.
OTHERAI-Based Facial Assessment
Real-time computer vision analysis using deep-learning-based landmark detection to track 468 facial points during standardized facial expressions.
Sponsors
Cairo University
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
25 Years to 40 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| AI-Based Composite Asymmetry Score. | Baseline. | The specific numerical output generated by the computer-vision system. It quantifies facial symmetry by measuring the amplitude of movement (in pixels/displacement) during five standardized facial expressions: brow lift, eye closure, broad smile, snarl, and lip pucker. |
Countries
Egypt
Contacts
CONTACTAli Noureldin Hassanein, B.Sc., PT.
PRINCIPAL_INVESTIGATORAli Noureldin Hassanein, B.Sc.
Cairo University
Outcome results
None listed