Abstract
New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Chongqing, People’s Republic of China, by NewsRx correspondents, research stated, “Parkinson’s disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice.” Financial support for this research came from Fundamental Research Funds for the Central Universities. Our news editors obtained a quote from the research from the Department of Radiology, “In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute.”
NETL
NSTL
Frontiers Media Sa