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采用面像图生成评估和呈现 House-Brackmann 及区域性面瘫分级的客观方法。

Objective method of assessing and presenting the House-Brackmann and regional grades of facial palsy by production of a facogram.

机构信息

Institute of Neurological Sciences, Southern General Hospital, Glasgow, Scotland. Brian.O'

出版信息

Otol Neurotol. 2010 Apr;31(3):486-91. doi: 10.1097/MAO.0b013e3181c993dc.

DOI:10.1097/MAO.0b013e3181c993dc
PMID:20042993
Abstract

OBJECTIVE

To produce a reliable objective method of assessing the House-Brackmann (H-B) and regional grades of facial palsy with the results produced and presented in a time and manner suitable for a routine clinical setting.

STUDY DESIGN

Analysis of video pixel data using artificial neural networks (ANNs).

SETTING

Tertiary-referral neuro-otologic center.

SUBJECTS

Subjects with varying degrees of unilateral facial palsy.

METHOD

Clinicians assessed videos of subjects with varying degrees of facial palsy performing prescribed movements. The results of their overall and regional assessments were used to train ANNs. These were then tested for consistency, accuracy, and ability to identify clinical changes in grading.

RESULTS

A group of subjects had their objective computer assessment repeated, and consistent H-B and regional grades were obtained. A second group had both subjective clinical and objective computer assessments performed. The program gave results that were within the expected level of agreement with the subjective clinical assessment for both H-B and regional grades. A third group had repeated clinical and computer assessments from the time of onset to recovery of facial function. The changes in the computer results both for H-B and regional grades tracked the clinical change.

CONCLUSION

It is possible to measure consistently and objectively the H-B and regional grades of facial palsy using trained ANNs to analysis video pixel data, and this can be done in a routine clinical environment by a technician. The results from each region of the face are presented as a Facogram along with the H-B grade.

摘要

目的

利用人工神经网络(ANNs)分析视频像素数据,生成一种可靠的客观方法,评估 House-Brackmann(H-B)和区域性面瘫分级,并以适合常规临床环境的时间和方式呈现结果。

研究设计

分析视频像素数据的人工神经网络(ANNs)。

地点

三级转诊神经耳科中心。

受试者

患有不同程度单侧面瘫的受试者。

方法

临床医生评估了患有不同程度面瘫的受试者执行规定动作的视频。他们对整体和区域性评估的结果用于训练人工神经网络(ANNs)。然后对其进行一致性、准确性和识别分级临床变化的能力进行测试。

结果

一组受试者的客观计算机评估结果被重复,获得了一致的 H-B 和区域性分级。第二组进行了主观临床和客观计算机评估。该程序的结果与主观临床评估在 H-B 和区域性分级方面具有预期的一致性水平。第三组从面瘫发作到面部功能恢复时进行了重复的临床和计算机评估。H-B 和区域性分级的计算机结果变化均与临床变化相吻合。

结论

使用经过训练的人工神经网络(ANNs)分析视频像素数据,可以一致且客观地测量面瘫的 H-B 和区域性分级,并且可以由技术员在常规临床环境中完成。面部每个区域的结果都与 H-B 分级一起作为 Facogram 呈现。

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