Facial Asymmetry Classification in Neurological Disorders: Integrating Computer Vision and Machine Learning for Improved Patient Care
Bibliographic record
Abstract
Facial gestures and movements play a critical role in communicating, eating, and expressing emotions, making the assessment of oro-facial functions vital in clinical practice. Neurological conditions such as stroke and facial palsy significantly impact these movements, necessitating accurate differentiation for appropriate diagnosis. This paper proposes an automated approach to classifying facial asymmetry in stroke and peripheral facial paralysis, leveraging computer vision and machine learning algorithms. Using public datasets like the Toronto NeuroFace and Massachusetts Eye and Ear Infirmary (MEEI), we employed facial landmark localization, comprehensive feature extraction, and classification techniques to discern subtle variations in facial movements and expressions across different conditions. Our approach achieved promising results, with accuracy up to 88% to distinguish facial impairments due to stroke and facial palsy. Tasks such as lip spreading, blinking, and eyebrow-raising demonstrated high accuracy, aligning with previous findings. Our study lays the groundwork for improving the diagnosis and treatment of oro-facial impairments using machine learning, with potential applications in clinical and emergency settings to enhance patient care and diagnostic accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".