PD-Diag-Net: Clinical-Priors guided Network on Brain MRI for Auxiliary Diagnosis of Parkinson's Disease

2025-09-28

AI Summary

Early diagnosis of Parkinson’s disease (PD) remains challenging due to reliance on subjective clinical assessments, often leading to delayed intervention. To address this, we propose PD-Diag-Net—a novel end-to-end MRI-based diagnostic framework explicitly integrating clinical prior knowledge. Our method introduces two biologically grounded priors: “region-wise functional connectivity” and “accelerated brain aging,” guiding feature aggregation and classification via a prior-aware architecture. The network incorporates standardized MRI preprocessing, region-specific attention mechanisms, and a brain-age deviation constraint, forming a dual-path interpretable architecture. Evaluated on multi-center data, PD-Diag-Net achieves 86% accuracy on external test sets and over 96% accuracy in early-stage PD identification—surpassing state-of-the-art methods by more than 20 percentage points. It significantly improves diagnostic robustness, subject-specific adaptability, and clinical interpretability through transparent, neurobiologically informed decision pathways.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Hardware-aware ML
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Parkinson's disease (PD) is a common neurodegenerative disorder that severely diminishes patients' quality of life. Its global prevalence has increased markedly in recent decades. Current diagnostic workflows are complex and heavily reliant on neurologists' expertise, often resulting in delays in early detection and missed opportunities for timely intervention. To address these issues, we propose an end-to-end automated diagnostic method for PD, termed PD-Diag-Net, which performs risk assessment and auxiliary diagnosis directly from raw MRI scans. This framework first introduces an MRI Pre-processing Module (MRI-Processor) to mitigate inter-subject and inter-scanner variability by flexibly integrating established medical imaging preprocessing tools. It then incorporates two forms of clinical prior knowledge: (1) Brain-Region-Relevance-Prior (Relevance-Prior), which specifies brain regions strongly associated with PD; and (2) Brain-Region-Aging-Prior (Aging-Prior), which reflects the accelerated aging typically observed in PD-associated regions. Building on these priors, we design two dedicated modules: the Relevance-Prior Guided Feature Aggregation Module (Aggregator), which guides the model to focus on PD-associated regions at the inter-subject level, and the Age-Prior Guided Diagnosis Module (Diagnoser), which leverages brain age gaps as auxiliary constraints at the intra-subject level to enhance diagnostic accuracy and clinical interpretability. Furthermore, we collected external test data from our collaborating hospital. Experimental results show that PD-Diag-Net achieves 86% accuracy on external tests and over 96% accuracy in early-stage diagnosis, outperforming existing advanced methods by more than 20%.

Problem

• Automates Parkinson's disease diagnosis using raw MRI scans
• Integrates clinical priors about brain regions and aging patterns
• Addresses variability in MRI data and improves early detection

Innovation

• Integrates clinical priors into MRI-based Parkinson's diagnosis
• Uses brain-region relevance and aging priors for feature aggregation
• Applies preprocessing to reduce MRI variability across subjects