TY  - EJOU
AU  - Ahmed, H. 
AU  - Ghannam, Naglaa E. 
AU  - Mancy, H. 
AU  - Mahareek, Esraa A. 

TI  - Subtle Micro-Tremor Fusion: A Cross-Modal AI Framework for Early Detection of Parkinson’s Disease from Voice and Handwriting Dynamics
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 2
SN  - 1526-1506

AB  - Parkinson’s disease remains a major clinical issue in terms of early detection, especially during its prodromal stage when symptoms are not evident or not distinct. To address this problem, we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage. We used 5 publicly accessible datasets, including UCI Parkinson’s Voice, Spiral Drawings, PaHaW, NewHandPD, and PPMI, and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation. The findings reveal that the model’s performance was superior and achieved 98.2%, a F1-score of 0.981, and AUC of 0.991 on the UCI Voice dataset. The model’s performance on the remaining datasets was also comparable, with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP, ILN–GNet, and CASENet. Across the evidence, the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an “early,” interpretable PD screening system.
KW  - Early Parkinson diagnosis; explainable AI (XAI); feature-level fusion; handwriting analysis; microtremor detection; multimodal fusion; Parkinson’s disease; prodromal detection; voice signal processing

DO  - 10.32604/cmes.2026.075732