DM-PAFNet: A dual-stage deep learning framework for Parkinson’s disease staging prediction via multi-modal MRI and clinical prior fusion

Abstract The early diagnosis and precise staging of disease severity in Parkinson’s disease (PD) represent significant challenges in current clinical practice, primarily limited by the subjectivity and instability of traditional assessment methods. In recent years, deep learning methods based on multimodal neuroimaging have offered a new avenue for objective and automated assessment; however, they still face key bottlenecks such as inefficient fusion of heterogeneous modalities and insufficient clinical plausibility of the models. To address these limitations, this study proposes an innovative two-stage multimodal deep learning framework, DM-PAFNet, which integrates a dual-branch multi-modal classification network (DB-MCNet) and a medical prior-guided attention fusion network (MP-AFNet). The framework is centered on a progressive strategy of ‘diagnosis for pre-training, staging for fine-tuning,’ modeling PD assessment as a hierarchical knowledge transfer process. In the first stage, we construct the DB-MCNet, which introduces a dual-stream adaptive fusion module to efficiently integrate information from T1-weighted imaging (T1-MRI) and diffusion tensor imaging. This stage achieves high-precision PD diagnosis and yields an image feature encoder with strong generalization capabilities. In the second stage, we propose the MP-AFNet. Building upon the inherited pre-trained weights, it utilizes a gated cross-modal attention module to fuse imaging features with clinical variables. Furthermore, it innovatively introduces a medically-informed constraint module, which embeds clinical knowledge as a soft constraint during training to enhance the credibility and stability of the staging predictions. Experimental results on the public PPMI dataset demonstrate the outstanding performance of our framework. The first-stage DB-MCNet achieved an accuracy of 95.85% in the PD vs healthy control classification task, with the learned imaging encoder subsequently transferred to initialize the second stage. In the more challenging Hoehn and Yahr (H&Y) staging prediction across 410 subjects (H&Y stages 0–3), the complete framework achieved an accuracy of 88.3%, significantly outperforming several mainstream baseline methods. In conclusion, by virtue of its two-stage structural design, efficient multimodal fusion mechanisms, and clinical prior-guided strategy, the DM-PAFNet provides an accurate, objective, and clinically interpretable solution for the automated diagnosis and fine-grained staging of PD.