Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder for which delayed recognition may limit timely clinical management. This study investigates a reproducible computer-aided screening approach based on facial motion analysis from standard RGB video recorded during the diadochokinetic /pataka/ task. Facial landmarks were extracted using a face-mesh model and mapped into spherical coordinates to represent facial motion trajectories. Coordinated facial behavior was characterized through pairwise Pearson correlation matrices computed between landmark trajectories, yielding correlation-based descriptors of inter-region motion patterns. We compared a domain-informed Manual-24 reference configuration with data-driven feature-selection strategies (ElasticNet and mRMR) under a leakage-aware nested cross-validation design using the Toronto NeuroFace dataset. Performance was reported as mean ± standard deviation across outer folds, with sensitivity emphasized because of its relevance for screening-oriented applications. The primary configuration (mRMR, k=3, ϕ + kNN) achieved 61.11 ± 19.24% accuracy, 61.11 ± 9.62% sensitivity, and 61.11 ± 34.70% specificity. These results suggest that correlation-derived coordination patterns contain discriminative information for ALS/HC separation, although fold-level variability indicates that performance should be interpreted cautiously. Task-aligned comparisons with prior /pataka/-based studies highlight the influence of sensing modality, evaluation level, and uncertainty reporting on apparent performance. Overall, correlation-based facial motion descriptors combined with leakage-aware feature selection provide a transparent proof-of-concept framework for RGB video-based ALS screening, motivating validation on larger cohorts and independent datasets.
Suárez-Hernández et al. (Thu,) studied this question.