Using Eye Aspect Ratio to Enhance Fast and Objective Assessment of Facial Paralysis A rapid and objective assessment of the severity of facial paralysis allows rehabilitation physicians to choose the optimal rehabilitation treatment regimen for their patients. In this study, patients with facial paralysis were enrolled as study objects, and the eye aspect ratio (EAR) index was proposed for the eye region. The correlation between EAR and the facial nerve grading system 2.0 (FNGS 2.0) score was analyzed to verify the ability of EAR to enhance FNGS 2.0 for the rapid and objective assessment of the severity of the facial paralysis. Firstly, in order to accurately calculate the EAR, we constructed a landmark detection model based on the face images of facial paralysis patients (FPFLDM). Evaluation results showed that the error rate of facial feature point detection in patients with facial paralysis of FPFLDM is 17.1%, which was significantly superior to the landmark detection model based on normal face images (NFFLDM). Secondly, in this study, the Frechet distance was used to calculate the difference in bilateral EAR of facial paralysis patients and to verify the correlation between this difference and the corresponding FNGS 2.0 score. The results showed that the higher the FNGS 2.0 score , the greater the difference in bilateral EAR. The correlation coefficient between the bilateral EAR difference and the corresponding FNGS 2.0 score was 0.9673, indicating a high correlation. Finally, through a 10fold crossvalidation, we can know that the accuracy of scoring the eyes of patients with facial paralysis using EAR was 85.7%, which can be used to enhance the objective and rapid assessment of the severity of facial paralysis by FNGS 2.0.

A Hybrid Evaluation System for Facial Paralysis Rehabilitation based on Machine Learning and Doctor Experience In the facial paralysis rehabilitation training progresses, evaluation processes waste huge efforts from both patients and doctors. In the meantime, doctorsxe2x80x99 subjective opinions cause inaccuracy result. Therefore, it is urgent to construct an objective grading system to acknowledge doctors of their patientsxe2x80x99 exact situation. In this paper, a method is present to construct a hybrid grading system based considering both machine learning results and doctor experience. Firstly, an online marking system based on the web is designed to collect and analyze samples. Then, evaluation models are constructed through TensorFlow from the samples from doctor to grade patients. Finally, various models are mixed to construct the hybrid evaluation system. Results are achieved, which can preliminarily evaluate the patients for their recovery conditions. Although the accuracy is not satisfied enough, it can be seen that the method is effective in facial paralysis rehabilitation training. With improvements in models, an automatic evaluation system will be applied to the rehabilitation of patients.