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Forehead Lesion Score for Facial Nerve Paralysis Evaluation

机译:前额病变评分用于面神经麻痹评估

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Facial nerve paralysis is mostly occurring from a temporary or permanent injury to any of facial nerves which drive the failure of facial nerve function. As a consequence, the facial muscles will not receive the necessary signals from the facial nerves in order to function properly and caused the weakness of facial muscles. This facial paralysis affects both verbal and non-verbal communication which has important function and essential element to the development of an individual. The subjective facial nerve assessment involves the assessment of forehead movement which designates whether the lesion is at the level of the upper motor neuron (has movement on both sides) or the lower motor neuron (no movement on paralysed side). An objective assessment of these lesions will give a great impact in managing the symptoms and limiting the sequelae of the paralysis compared to the subjective assessment. A forehead lesion score was developed in this study to differentiate the two types of lesion of facial paralysis, Upper Motor Neuron (UMN) and Lower Motor Neuron (UMN) lesion. Two potential features were extracted from Gabor filter, Mean Amplitude and Local Energy. The best scale and orientation of these features were chosen after the selection of best threshold value. The accuracy percentage by using the Local Energy feature in classifying the UMN is 100% and LMN lesion is 73%. From the results, a forehead lesion score is developed. As a conclusion, this lesion score has the outstanding ability in specifying the facial lesion types and may become a great aid to clinicians for an efficient facial nerve evaluation.
机译:面神经麻痹主要是由于对导致面神经功能衰竭的面神经的暂时或永久性损伤而引起的。结果,面肌将不会从面神经接收到必要的信号,以使其正常运行并导致面肌无力。面部麻痹会影响言语和非言语交际,这对于个人的成长具有重要的功能和必要的要素。主观面部神经评估涉及额头运动的评估,该评估指示病变位于上运动神经元(两侧运动)还是下运动神经元(瘫痪侧运动)水平。与主观评估相比,对这些病变的客观评估将在管理症状和限制麻痹后遗症方面产生重大影响。在这项研究中开发了一个前额病变评分,以区分两种类型的面部麻痹病变:上运动神经元(UMN)和下运动神经元(UMN)病变。从Gabor滤波器中提取了两个潜在特征:平均幅度和局部能量。在选择最佳阈值之后,选择了这些特征的最佳比例和方向。通过使用本地能量功能对UMN进行分类的准确度百分比为100%,LMN病变为73%。从结果可以得出前额病变评分。综上所述,该病灶评分具有指定面部病灶类型的出色能力,并且可能对临床医生进行有效的面部神经评估有很大帮助。

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