@article{Ahmed2026, 
author = {H. Ahmed and Naglaa E. Ghannam and H. Mancy and Esraa A. Mahareek},
title = {Subtle Micro-Tremor Fusion: A Cross-Modal AI Framework for Early Detection of Parkinson’s Disease from Voice and Handwriting Dynamics},
year = {2026},
journal = {Computer Modeling in Engineering & Sciences},
volume = {146},
number = {2},
pages = {38},
keywords = {Early Parkinson diagnosis, explainable AI (XAI), feature-level fusion, handwriting analysis, microtremor detection, multimodal fusion, Parkinson’s disease, prodromal detection, voice signal processing},
url = {https://www.sciopen.com/article/10.32604/cmes.2026.075732},
doi = {10.32604/cmes.2026.075732},
abstract = {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.}
}