Enhancing early Parkinson’s disease detection through multimodal deep learning and explainable AI: insights from the PPMI database
Open accessAbstract
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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Dentamaro et al. (2024) achieved >90% accuracy for prodromal PD detection using a 3D-CNN architecture that synthesizes structural MRI data, cardinal motor symptoms, and genetic risk markers.
“Dentamaro et al. (2024) achieved >90% accuracy for prodromal PD detection using a 3D-CNN architecture that synthesizes structural MRI data, cardinal motor symptoms (tremor/rigidity/bradykinesia/postural instability), and genetic risk markers”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Dentamaro et al., 2024
Results (Models evaluation, Table 1; page unavailable) report 96.6% accuracy for DenseNet + EN in classifying prodromal PD versus healthy controls using MRI and clinical data. Methods (Clinical dataset, Table 3; page unavailable) list the four cardinal motor symptoms and family history among the inputs, but no genetic markers. The Discussion says integrating genetic data is future work, so the claim incorrectly describes family-history features as genetic risk markers. (LLM, conf=0.91)
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- Authors
- Dentamaro, V., Impedovo, D., Musti, L., Pirlo, G., & Taurisano, P.
- Affiliations
- University of Bari Aldo Moro (IT)
- Corresponding author
- Dentamaro, Vincenzo
- Venue
- Scientific Reports
- Published
- 2024-09-09
- Volume
- 14
- Issue
- 1
- Pages
- 20941-20941
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11385236
- OpenAlex
- W4402377819
- Cited by
- 116
- References
- 82
- Citations by year
- 2026: 68 · 2025: 45 · 2024: 3
- FWCI
- 31.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- DARE - DigitAl lifelong pRevEntion (B53C22006420001)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Parkinson's disease, multimodal fusion, multimodal deep learning, explainable AI, DenseNet, vision transformer, integrated gradients, attention heatmaps, Parkinson's Progression Markers Initiative, neuroimaging, clinical data, biomarkers, cognitive decline, lateral ventricle, temporal lobe, prefrontal cortex
- MeSH terms
- Deep Learning, Aged, Artificial Intelligence, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Parkinson Disease, Databases, Factual, Neural Networks, Computer, Early Diagnosis
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