Early Diagnosis of Parkinson's Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristic

Abstract

Parkinson’s disease (PD) is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging (MRI) scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to use deep learning to diagnose PD severity by combining symptoms data and MRI data, all of which comes from the public Parkinson’s Progression Markers Initiative (PPMI) database, in order to provide specialists and patients with more flexibility. A new hybrid model architecture was implemented to fully utilize both forms of clinical data to evaluate PD severity with high accuracy, and models based on only symptoms and only MRI scans were also developed. The developed model integrates a fully connected deep learning neural network for symptoms data training and a transfer learning-based convolutional neural network for MRI scans training. Instead of performing only binary classification, all models classify patients into five severity categories, with stage zero representing healthy patients and stages four and five representing patients with PD. The symptoms-only, MRI scans-only and hybrid models achieved accuracies of 0.77, 0.68, and 0.94, respectively. The hybrid model also had high precision and recall scores of 0.94 and 0.95. Real clinical cases confirm the hybrid model’s strong performance, where patients were classified incorrectly with both other models but correctly by the hybrid. It is also consistent across the five 0-4 severity stages, so early detection of PD is accurate.