ArticleScientific reports2025
Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Multimodal Electrophysiological Signals for Machine Learning-Aided Parkinson's Disease Diagnosis.Biosensors · 2026Article
- A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study.JMIR medical informatics · 2026Article
- Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task.Brain sciences · 2026Article
- Harnessing artificial intelligence: Revolutionizing clinical care for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Hybrid deep learning novel framework for classification of parkinson's disease.Scientific reports · 2026Article
- MIL-O-PD: a two-stage multiple instance learning and heuristic optimization framework for unpaired multimodal Parkinson's diagnosis.Frontiers in aging neuroscience · 2026Article
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
- MultimodalCNN-PD: a Parkinson's disease diagnostics framework using multimodal convolutional neural network.Frontiers in aging neuroscience · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder that remained challenging for proper diagnosis in its early stages due to its heterogeneous symptom presentation and overlapping clinical features. Consequently, there is no consensus on effectively detecting early-stage PD and classifying motor symptom severity. Therefore, the proposed research introduced MultiParkNet, an avant-grade multi-modal deep learning framework for early-stage PD detection synthesizing diverse neurological and physiological data sources. The proposed system integrated audio speech patterns, motor skills drawing characteristics, neuroimaging data, and cardiovascular signals with different neural architectures for robust feature extraction and fusion. The probabilistic classification approach enhanced disease identification with high fidelity and early detection. The model demonstrated exceptional performance, with an average training accuracy of 99.67%, validation accuracy of 98.15% [Formula: see text] and test accuracy of 96.74% [Formula: see text] across cross-validation experiments. This novel architecture significantly improved diagnostic precision with a transformative, AI-driven approach for Parkinson's disease assessment and potential clinical implications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.