Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging

Abstract

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

“…The number of patients in stages I to V was 22, 27, 53, 87, and 7, respectively. More details on the exclusion criteria, imaging instrument, and experimental design can be found in Hsu et al. 21 This clinical study was approved by the Medical Ethics Committee of E-Da Hospital. All patients signed written informed consent before participating.…”

A Shape Approximation for Medical Imaging Data

“…Next, 6 well-known classification methods, which are support vector machine (SVM), naive Bayes (NB), random forests (RF), XGboost (XGB), logistic regression (LR), and linear discriminant analysis (LDA), are used to learn classifiers for PD imaging classification with the above 6 sets of features separately [ 3 , 9 , 23 , 24 , 25 ]. The comparison study is conducted under the following repeated hold-out with 100 rounds of 5-fold cross-validation (CV) and stratified sampling scheme.…”

“…Prashanth et al [ 8 ] proposed to draw ‘indirect 3D features’ by building a regression surface of an ROI based on the 2D-combined image. Hsu et al [ 9 ] found that the classification performance for identifying PD is improved by calculating the volumes of an ROI in 3D space. Cheng et al [ 10 ] adopted the 3D features such as volume, surface area, and diameter to describe the geometric characteristics of the volumes of interest to assist the diagnosis of idiopathic PD.…”

“…Most of the aforementioned studies of drawing 3D features focused on computing the volumes of the ROI and applying the quantities derived from the volumes of the ROI to PD identification [ 6 , 7 , 9 , 10 , 11 ]. Although the geometric features such as volume and the volume-related quantities are valuable proxies representing the 3D shapes of the ROI, they still cannot fully capture the desired shapes since different shapes of the ROI could have the same volumes or volume-related quantities.…”

“…Six popular classification methods such as logistic regression (LR), linear discriminant analysis (LDA), naive Bayes (NB), random forests (RF), support vector machine (SVM), and extreme gradient boost (XGB) are adopted to learn classifiers for PD using the coefficients contained in the 2D and 3D shape-fitting equations. The 6 classifiers are usually adopted in many comparison studies for medical data [ 3 , 9 , 23 , 24 , 25 ]. The numerical results reveal that these coefficients did improve the classification accuracies for most of the considered classifiers.…”