NeuroAid An Open-Data Multimodal Screening Framework for Parkinson's and Depression Risk Estimation
TL;DR
NeuroAid, an open-data multimodal AI screening framework that estimates condition-specific risk from non-invasive, accessible signals spanning acoustic speech biomarkers, facial and video-based affective cues, and clinical or behavioral digital biomarkers, is presented.
Abstract
Neurological and mental-health conditions such as Parkinson's disease (PD) and major depressive disorder (MDD) impose a substantial and growing global burden, yet reliable early screening remains largely confined to specialist clinical settings that are inaccessible to the majority of affected individuals. We present NeuroAid, an open-data multimodal AI screening framework that estimates condition-specific risk from non-invasive, accessible signals spanning acoustic speech biomarkers, facial and video-based affective cues, and clinical or behavioral digital biomarkers. NeuroAid is organized as a modular, branch-wise pipeline covering three independent signal pathways: audio, vision, and behavioral, unified by a frozen-embedding late-fusion layer that produces interpretable joint risk scores. A participant-safe, subject-grouped splitting protocol is enforced throughout, preventing inter-subject data leakage, a frequently overlooked cause of artificially inflated performance in clinical machine learning benchmarks. On the Figshare Parkinson's audio dataset, a proposed small-data protocol combining frozen WavLM foundation-model embeddings with a grouped SVM-RBF classifier achieves a cross-validated balanced accuracy of 0.786 +/- 0.073 and a held-out test balanced accuracy of 75.0%, an F1-score of 80.0%, and an AUC-ROC of 82.8%. The depression vision branch, trained on the DepVidMood corpus via transfer learning from FER-2013, reaches a threshold-tuned test balanced accuracy of 59.7% and is presented as an honest hard-case baseline under severe class imbalance. NeuroAid is further distinguished by its production-grade MLOps scaffolding, including orchestrated branch training, JSON and Markdown artifact reporting, a deployable Streamlit screening interface, and a complete CI/CD workflow. The entire system is built exclusively on publicly available datasets, ensuring full reproducibility. All code, artifacts, and benchmark outputs are versioned and deployable via Docker.