Screening Patients with Early Stage Parkinson’s Disease Using a Machine Learning Technique: Measuring the Amount of Iron in the Basal Ganglia

Seon Lee,Chaewon Shin,Jung-Hyo Rhim,Jee-Young Lee,Jeehun Kim,Sun-Won Park,Joon-Yul Choi,Se-Hong Oh

doi:10.3390/app10238732

Abstract

The purpose of this study was to determine whether a support vector machine (SVM) model based on quantitative susceptibility mapping (QSM) can be used to differentiate iron accumulation in the deep grey matter of early Parkinson’s disease (PD) patients from healthy controls (HC) and Non-Motor Symptoms Scale (NMSS) scores in early PD patients. QSM values on magnetic resonance imaging (MRI) were obtained for 24 early PD patients and 27 age-matched HCs. The mean QSM values in deep grey matter areas were used to construct SVM and logistic regression (LR) models to differentiate between early PD patients and HCs. Additional SVM and LR models were constructed to differentiate between low and high NMSS scores groups. A paired t-test was used to assess the classification results. For the differentiation between early PD patients and HCs, SVM had an accuracy of 0.79 ± 0.07, and LR had an accuracy of 0.73 ± 0.03 (p = 0.027). SVM for NMSS classification had a fairly high accuracy of 0.79 ± 0.03, while LR had 0.76 ± 0.04. An SVM model based on QSM offers competitive accuracy for screening early PD patients and evaluates non-motor symptoms, which may offer clinicians the ability to assess the progression of motor symptoms in the patient population.

Highlights

Effective screening for early Parkinson’s disease (PD) is essential, as early diagnosis and treatment can postpone the progression of symptoms and complications caused by the disease [1]
Recent have demonstrated that iron accumulation in the basal ganglia may serve as a potential biomarker of magnetic resonance imaging (MRI) studies have demonstrated that iron accumulation in the basal ganglia may serve as a potential
We found that Quantitative susceptibly mapping (QSM) values were higher in patients with early PD than in healthy controls (HC) in all regions of interest (ROIs) (GP, RN, DN, SN pars reticulate (SNr), SN pars compacta (SNc), CN, and PUT)

Summary

Introduction

Effective screening for early Parkinson’s disease (PD) is essential, as early diagnosis and treatment can postpone the progression of symptoms and complications caused by the disease [1]. Despite the importance of early diagnosis, PD is still under-recognized in its early stages [3], perhaps partially because existing diagnostic criteria are mainly based on subjective symptoms [4]. In the early stages of PD, nonmotor symptoms (NMS), such as olfactory problems, depression, and rapid eye movement sleep disorder, are more prominent than motor symptoms, further complicating the early diagnosis of this disease [3,5]. Quantitative susceptibly mapping (QSM) on magnetic resonance imaging (MRI) may be useful in detecting this iron accumulation [9]

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