Sensitivity and specificity of an eye movement tracking-based biomarker for concussion.

Q3 Medicine

Concussion Pub Date : 2015-08-06 eCollection Date: 2016-03-01 DOI:10.2217/cnc.15.3

Uzma Samadani, Meng Li, Meng Qian, Eugene Laska, Robert Ritlop, Radek Kolecki, Marleen Reyes, Lindsey Altomare, Je Yeong Sone, Aylin Adem, Paul Huang, Douglas Kondziolka, Stephen Wall, Spiros Frangos, Charles Marmar

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引用次数: 36

Abstract

Object: The purpose of the current study is to determine the sensitivity and specificity of an eye tracking method as a classifier for identifying concussion.

Methods: Brain injured and control subjects prospectively underwent both eye tracking and Sport Concussion Assessment Tool 3. The results of eye tracking biomarker based classifier models were then validated against a dataset of individuals not used in building a model. The area under the curve (AUC) of receiver operating characteristics was examined.

Results: An optimal classifier based on best subset had an AUC of 0.878, and a cross-validated AUC of 0.852 in CT- subjects and an AUC of 0.831 in a validation dataset. The optimal misclassification rate in an external dataset (n = 254) was 13%.

Conclusion: If one defines concussion based on history, examination, radiographic and Sport Concussion Assessment Tool 3 criteria, it is possible to generate an eye tracking based biomarker that enables detection of concussion with reasonably high sensitivity and specificity.

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基于眼动追踪的脑震荡生物标志物的敏感性和特异性。

目的:本研究的目的是确定眼动追踪方法作为识别脑震荡分类器的敏感性和特异性。方法:对脑损伤者和对照组进行前瞻性眼动追踪和运动脑震荡评估工具3。然后将基于生物标记的眼动追踪分类器模型的结果与未用于构建模型的个体数据集进行验证。测试了接收机工作特性的曲线下面积(AUC)。结果:基于最佳子集的最优分类器的AUC为0.878,CT-受试者的交叉验证AUC为0.852，验证数据集的AUC为0.831。外部数据集(n = 254)的最佳误分类率为13%。结论:如果一个人根据病史、检查、x线摄影和运动脑震荡评估工具3的标准来定义脑震荡，就有可能产生一种基于眼动追踪的生物标志物，使脑震荡的检测具有相当高的灵敏度和特异性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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