ABSTRACT
BACKGROUND: Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD. METHODS: We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson's Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation. RESULTS: We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774. CONCLUSIONS: Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.
ABSTRACT
With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.
