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September 10, 2025NeuroImage ClinicalOpen Access

Multi-Center 3D CNN for Parkinson’s disease diagnosis and prognosis using clinical and T1-weighted MRI data

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Authors

SBSilvia BasaiaESElisabetta SarassoFSFrancesco Sciancalepore

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Overview

Observational analysis improved diagnosis accuracy and predicted progression in Parkinson's disease, indicating potential clinical applications.

Key Points

  • The model achieved 74% accuracy in distinguishing moderate-to-severe PD from controls using MRI data alone.
  • Transfer learning enhanced classification performance, resulting in 64% accuracy for mild-PD versus controls.
  • Over 70% accuracy was achieved for disease progression prediction by combining MRI and clinical data.
  • Activation maps provided insights into critical brain regions influencing CNN decisions, allowing for personalized monitoring.

Cite This Study

Basaia et al. (2025) studied this question.

synapsesocial.com/papers/68c1b60d54b1d3bfb60eb2e3https://doi.org/10.1016/j.nicl.2025.103859
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Combining convolution neural networks with long‐short term memory layers to predict Parkinson's disease progression2024 · 11 citations
  2. 2MultimodalCNN-PD: a Parkinson’s disease diagnostics framework using multimodal convolutional neural network2026
  3. 3Innovative Deep Learning Approach for Parkinson's Disease Prediction: Leveraging Convolutional Neural Networks for Early Detection2024 · 3 citations
  4. 4Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network2024 · 22 citations
  5. 5Machine learning based Parkinson’s disease detection and progression evaluation using gray and white matter segmentation from 3D MRI2024 · 3 citations