Automated MRI-Based Classification of Parkinsonism: A Deep Learning Approach to Distinguish PD From PSP.

OBJECTIVE

Differentiating Parkinson's disease (PD) from progressive supranuclear palsy (PSP) is crucial for appropriate treatment, as each disease has distinct therapeutic requirements. The Magnetic Resonance Parkinsonism Index (MRPI) has shown promise as a diagnostic biomarker, yet manual methods introduce variability and limit its applicability. In this study, we aim to develop a fully automated algorithm for MRPI 1.0 and 2.0 calculation, and assess its ability to distinguish PD from PSP in two cohorts from different regions of China.

METHODS

A total of 75 PD patients and 29 PSP patients from two hospitals were enrolled. All participants underwent neurological examinations, including the MDS-UPDRS-III and H-Y scale, as well as brain MRI scans. Additionally, tissue-intensity images derived from 3D isotropic T1WI images from 2D thick slices using a deep learning (DL)-based super-resolution (SR) technique were aligned to a standard template followed by corresponding structural mask parcellation for measurement of MRPI 1.0 and MRPI 2.0. Subsequently, a logistic regression model was constructed to identify PD patients from PSP based on these indexes.

RESULTS

MRPI 2.0 demonstrated higher diagnostic accuracy than MRPI 1.0, with an AUC of 0.78. Additionally, the automated method showed strong linear correlations with manual assessments from an experienced radiologist, validating its reliability, and identification of PSP from PD with the average AUC of 0.85.

CONCLUSION

The automated MRPI method improves diagnostic accuracy for differentiating PD from PSP, providing a reliable and clinically applicable tool. The integration of a super-resolution technique to convert 2D MRI data into high-resolution images expands the potential of MRPI as a neuroimaging biomarker.