Machine Learning Prediction of Rapid Parkinson Disease Progression Using Combined Imaging and Clinical Biomarkers

Machine learning models, particularly SVM, demonstrate strong predictive performance in identifying rapid PD progression using multimodal clinical and imaging biomarkers. The integration of SPECT imaging with clinical assessments improves disease trajectory characterization, potentially supporting personalized treatment strategies and optimizing clinical trial design. Future studies should incorporate additional biomarkers and longer follow-up durations to enhance model generalizability.