Using Eye Aspect Ratio to Enhance Fast and Objective Assessment of Facial Paralysis

Abstract

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 (FP-FLDM). Evaluation results showed that the error rate of facial feature point detection in patients with facial paralysis of FP-FLDM is 17.1%, which was significantly superior to the landmark detection model based on normal face images (NF-FLDM). Secondly, in this study, the Fréchet 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 10-fold 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.

The Feasibility of An Automatical Facial Evaluation System Providing Objective and Reliable Results for Facial Palsy

“…A growing body of studies have focused on local asymmetries, which included regional asymmetry and angular asymmetry [25], [60]. However, most studies used it as important data in algorithm processing, and only a few studies input local asymmetry as a result for clinical reference [25], [61], [62]. In our study, indicators reflecting the movements of the patient's facial regions (eyebrows, eyes, and lips) and the severity of synkinesis in FP patients were suggested.…”

Ensemble Stacking for Grading Facial Paralysis Through Statistical Analysis of Facial Features

“…There are also several strategies based on eye-related features that have been proposed [11,21]. Facial points and iris regions are extracted using an ensemble of regression trees from the images [20].…”

“…A meticulous assessment of the efficacy and resilience of the model developed for detecting FP was undertaken, involving a comprehensive analysis of several supervised learning algorithms, notably LR, DT, NB, and SVM. These algorithms were selected based on their demonstrated proficiency in classifying data in previous studies, along with their prevalent application in the diagnosis of FP [2,11,[19][20][21]. It is acknowledged that the choice of machine learning algorithm is contingent upon the nature of the dataset and the computational efficiency of the algorithms under consideration.…”

“…In related studies, various approaches like landmarks [4,6], facial action units [7,8], eye movement features [9][10][11], and convolutional neural networks (CNN) [12][13][14] are used in the feature extraction stage from the FP images and videos. This paper uses novel approaches to detect FP and its grade by combining multiple facial features (landmarks, facial action units, and eye movement features) to achieve better FP detection.…”