School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Shenzhen Institute of Neuroscience, Shenzhen, China.
Comput Math Methods Med. 2020 Jan 29;2020:1038906. doi: 10.1155/2020/1038906. eCollection 2020.
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.
一种快速、客观的面瘫严重程度评估方法，可以使康复医师为患者选择最佳的康复治疗方案。本研究纳入面瘫患者作为研究对象，提出了眼部眼裂比（EAR）指数，分析 EAR 与面神经分级系统 2.0（FNGS 2.0）评分的相关性，验证 EAR 增强 FNGS 2.0 对面瘫严重程度快速、客观评估的能力。首先，为了准确计算 EAR，我们构建了基于面瘫患者面部图像的地标检测模型（FP-FLDM）。评估结果表明，FP-FLDM 对面瘫患者面部特征点检测的错误率为 17.1%，明显优于基于正常面部图像的地标检测模型（NF-FLDM）。其次，本研究采用 Fréchet 距离计算面瘫患者双侧 EAR 的差异，验证双侧 EAR 差异与相应 FNGS 2.0 评分的相关性。结果表明，FNGS 2.0 评分越高，双侧 EAR 的差异越大。双侧 EAR 差异与相应 FNGS 2.0 评分的相关系数为 0.9673，相关性较高。最后，通过 10 折交叉验证可知，EAR 对面瘫患者眼部评分的准确率为 85.7%，可以增强 FNGS 2.0 对面瘫严重程度的客观快速评估。
