Video-Based Facial Movement Analysis in the Assessment of Bulbar Amyotrophic Lateral Sclerosis: Clinical Validation
Bibliographic record
Abstract
Purpose: Facial movement analysis during facial gestures and speech provides clinically useful information for assessing bulbar amyotrophic lateral sclerosis (ALS). However, current kinematic methods have limited clinical application due to the equipment costs. Recent advancements in consumer-grade hardware and machine/deep learning made it possible to estimate facial movements from videos. This study aimed to establish the clinical validity of a video-based facial analysis for disease staging classification and estimation of clinical scores. Method: Fifteen individuals with ALS and 11 controls participated in this study. Participants with ALS were stratified into early and late bulbar ALS groups based on their speaking rate. Participants were recorded with a three-dimensional (3D) camera (color + depth) while repeating a simple sentence 10 times. The lips and jaw movements were estimated, and features related to sentence duration and facial movements were used to train a machine learning model for multiclass classification and to predict the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised (ALSFRS-R) bulbar subscore and speaking rate. Results: The classification model successfully separated healthy controls, the early ALS group, and the late ALS group with an overall accuracy of 96.1%. Video-based features demonstrated a high ability to estimate the speaking rate (adjustedR2= .82) and a moderate ability to predict the ALSFRS-R bulbar subscore (adjustedR2= .55). Conclusions: The proposed approach based on a 3D camera and machine learning algorithms represents an easy-to-use and inexpensive system that can be included as part of a clinical assessment of bulbar ALS to integrate facial movement analysis with other clinical data seamlessly
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".