AI and Neurological Rare & Neuroimmune Diseases ── Hereditary neurodegeneration ── 2026-10-26
Hereditary neurodegenerative diseases: treatment, trials and AI
- Abstract
BackgroundCerebellar ataxia is characterized by abnormalities in gait, speech, ocular movement, and coordination, making accurate detection challenging.PurposeThis study develops a lightweight multimodal Deep Learning (DL)-based framework for cerebellar ataxia detection.Research DesignThe proposed approach integrates voice, gaze, and body pose features from independent public datasets and performs class-level multimodal feature fusion to capture complementary motor and behavioral cues. Fine-grained features such as Mel Frequency Cepstral Coefficients (MFCCs) and vocal perturbation measures, gaze fixation dynamics, and spatiotemporal body pose features are extracted using Python-compatible tools. Each modality is processed with a specific Multilayer Perceptron (MLP)-based encoder to produce small embeddings, which are fused through feature concatenation and processed by a fully connected MLP classifier. To ensure stable convergence and robust generalization capability on limited and noisy clinical data, a novel hybrid optimizer, Adaptive Sharp-Lookahead Lite, is used.ResultsExperimental results demonstrate that the proposed framework achieves an accuracy of 99.56% in distinguishing ataxia from normal cases under the adopted experimental setting.ConclusionsThe findings demonstrate the potential of the framework as a lightweight AI-based screening approach for resource-constrained settings.
Journal IF-equivalent: 2.5 (OpenAlex 2-year mean citedness, value as of 2026-10-25, retrieved 2026-10-26; not the official Clarivate IF)Reference: A N, V S. Lightweight Multimodal Deep Learning Framework for Cerebellar Ataxia Detection Using an MLP Encoder and Hybrid Optimizer. Percept Mot Skills. 2026 Oct 24 [Epub ahead of print]. doi:10.1177/00315125261501241. PMID: 42925486.Checked: Abstract only