Open Access

ARTICLE

Subtle Micro-Tremor Fusion: A Cross-Modal AI Framework for Early Detection of Parkinson’s Disease from Voice and Handwriting Dynamics

H. Ahmed¹, Naglaa E. Ghannam^2,*, H. Mancy³, Esraa A. Mahareek⁴

1 Mathematics Department, College of Science and Humanities in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
2 Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
3 Department of Computer Science, College of Engineering and Computer Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
4 Department of Mathematics, Faculty of Science, Al-Azhar University (Girls’ Branch), Cairo, Egypt

* Corresponding Author: Naglaa E. Ghannam. Email:

(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences 2026, 146(2), 38 https://doi.org/10.32604/cmes.2026.075732

Received 07 November 2025; Accepted 20 January 2026; Issue published 26 February 2026

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.

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Keywords

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