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

Applied Sciences

Se-Hong Oh¹  Jeehun Kim²  Chaewon Shin³  Jee-Young Lee⁴  Sun-Won Park⁵  JungHyo Rhim⁶  JoonYul Choi⁷  Seon Lee⁸ 

[1] Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA;Department of Neurology, College of Medicine, Chungnam National University, Daejeon 35015, Korea;Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul 07061, Korea;Department of Radiology, College of Medicine, Seoul National University, Seoul 03080, Korea;Department of Radiology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul 07061, Korea;Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA;Sungkyunkwan University School of Medicine, Sungkyunkwan University, Seoul 06355, Korea;

关键词: Parkinson’s disease;    machine learning;    support vector machine;    quantitative susceptibility mapping;    nonmotor symptom;   

DOI  :  10.3390/app10238732

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown