Towards the identification of Parkinson's Disease using only T1 MR Images

TL;DR

This study explores the potential of using T1 MRI scans combined with machine learning classifiers to automatically diagnose Parkinson's Disease, aiming for earlier detection and improved patient outcomes.

Contribution

The paper introduces a new pipeline utilizing FreeSurfer for feature extraction and compares multiple classifiers for PD diagnosis using MRI data.

Findings

Support Vector Machine achieved the highest accuracy among classifiers.

The method shows promising results in differentiating PD from healthy controls.

MRI-based features can effectively assist in early Parkinson's diagnosis.

Abstract

Parkinson's Disease (PD) is one of the most common types of neurological diseases caused by progressive degeneration of dopamin- ergic neurons in the brain. Even though there is no fixed cure for this neurodegenerative disease, earlier diagnosis followed by earlier treatment can help patients have a better quality of life. Magnetic Resonance Imag- ing (MRI) has been one of the most popular diagnostic tool in recent years because it avoids harmful radiations. In this paper, we investi- gate the plausibility of using MRIs for automatically diagnosing PD. Our proposed method has three main steps : 1) Preprocessing, 2) Fea- ture Extraction, and 3) Classification. The FreeSurfer library is used for the first and the second steps. For classification, three main types of classifiers, including Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM), are applied and their classification abil- ity is compared. The Parkinsons Progression Markers Initiative (PPMI) data set is used to evaluate the proposed method. The proposed system prove to be promising in assisting the diagnosis of PD.