一种用于自动眼球震颤敏锐度功能分析的新算法。
A new algorithm for automated nystagmus acuity function analysis.
机构信息
The Laboratory of Visual and Ocular Motor Neurophysiology, The UPMC and Children’s Eye Center, Department of Ophthalmology, Pediatric Ophthalmology, The Children’s Hospital of Pittsburgh, University of Pittsburgh, USA.
出版信息
Br J Ophthalmol. 2011 Jun;95(6):832-6. doi: 10.1136/bjo.2010.184432. Epub 2010 Nov 7.
AIMS
We developed a new data analysis algorithm called the automated nystagmus acuity function (ANAF) to automatically assess nystagmus acuity function. We compared results from the ANAF with those of the well-known expanded nystagmus acuity function (NAFX).
METHODS
Using the ANAF and NAFX, we analysed 60 segments of nystagmus data collected with a video-based eye tracking system (EyeLink 1000) from 30 patients with infantile or mal-development fusional nystagmus. The ANAF algorithm used the best-foveation positions (not true foveation positions) and all data points in each nystagmus cycle to calculate a nystagmus acuity function.
RESULTS
The ANAF automatically produced a nystagmus acuity function in a few seconds because manual identification of foveation eye positions is not required. A structural equation model was used to compare the ANAF and NAFX. Both ANAF and NAFX have similar measurement imprecision and relatively little bias. The estimated bias was not statistically significant for either methods or replicates.
CONCLUSIONS
We conclude that the ANAF is a valid and efficient algorithm for determining a nystagmus acuity function.
目的
我们开发了一种新的数据分析算法，称为自动眼震敏锐度函数（ANAF），用于自动评估眼震敏锐度功能。我们将 ANAF 的结果与著名的扩展眼震敏锐度函数（NAFX）进行了比较。
方法
使用 ANAF 和 NAFX，我们分析了 30 名患有婴儿或发育性融合性眼震的患者使用基于视频的眼动跟踪系统（EyeLink 1000）收集的 60 段眼震数据。ANAF 算法使用最佳注视位置（非真实注视位置）和每个眼震周期中的所有数据点来计算眼震敏锐度函数。
结果
ANAF 可以在几秒钟内自动生成眼震敏锐度函数，因为不需要手动识别注视眼位置。我们使用结构方程模型比较了 ANAF 和 NAFX。ANAF 和 NAFX 的测量精度相似，且相对偏差较小。两种方法或重复测量的估计偏差均无统计学意义。
结论
我们得出结论，ANAF 是一种确定眼震敏锐度函数的有效且高效的算法。
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