ArticleScientific reports2024
Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Integrating retinal and brain imaging biomarkers for diagnosis of parkinson's disease: findings from a time-lagged cross-sectional persian cohort study.Scientific reports · 2026Article
- A multimodal explainable artificial intelligence framework for interpretable Parkinson's disease prediction.Scientific reports · 2026Article
- QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.Scientific reports · 2026Article
- Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.Journal of molecular neuroscience : MN · 2026Article
- Hybrid deep learning novel framework for classification of parkinson's disease.Scientific reports · 2026Article
- Facial Expression Annotation and Analytics for Dysarthria Severity Classification.Sensors (Basel, Switzerland) · 2026Article
- Early Diagnosis of Parkinson's Disease Through Lite HGWA-Net Model: A Hybrid CNN Based on Wavelet Transform and Attention Mechanism.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Trends and challenges in imaging research of Parkinson's disease: a 10-year bibliometric analysis.Psychoradiology · 2026Article
- AGMNet: an explainable gated multimodal framework integrating MRI heterogeneity and dopaminergic asymmetry for Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- SPARROW: subtyping Parkinson's disease with agentic reasoning and robust omics workflow.NPJ artificial intelligence · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- MultimodalCNN-PD: a Parkinson's disease diagnostics framework using multimodal convolutional neural network.Frontiers in aging neuroscience · 2026Article
- Explainable multimodal feature fusion networks for Parkinson's disease prediction.Frontiers in digital health · 2026Article
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Dynamic context-aware multi-modal deep learning for longitudinal prediction of Parkinson's disease progression.Scientific reports · 2025Article
- Listening to Patients' Voices on the Use of AI in Health Care: Cross-Sectional Study.Journal of medical Internet research · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Parkinson's is the second most common neurodegenerative disease, affecting nearly 8.5M people and steadily increasing. In this research, Multimodal Deep Learning is investigated for the Prodromal stage detection of Parkinson's Disease (PD), combining different 3D architectures with the novel Excitation Network (EN) and supported by Explainable Artificial Intelligence (XAI) techniques. Utilizing data from the Parkinson's Progression Markers Initiative, this study introduces a joint co-learning approach for multimodal fusion, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities. DenseNet with EN outperformed other models, showing a substantial increase in accuracy when supplemented with clinical data. XAI methods, such as Integrated Gradients for ResNet and DenseNet, and Attention Heatmaps for Vision Transformer (ViT), revealed that DenseNet focused on brain regions believed to be critical to prodromal pathophysiology, including the right temporal and left pre-frontal areas. Similarly, ViT highlighted the lateral ventricles associated with cognitive decline, indicating their potential in the Prodromal stage. These findings underscore the potential of these regions as early-stage PD biomarkers and showcase the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis, paving the way for innovative diagnostic tools and precision medicine.
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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.