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正常眼与圆锥角膜眼中的角膜对称性

Corneal enantiomorphism in normal and keratoconic eyes.

作者信息

Saad Alain, Guilbert Emmanuel, Gatinel Damien

出版信息

J Refract Surg. 2014 Aug;30(8):542-7. doi: 10.3928/1081597X-20140711-07.

Abstract

PURPOSE

To evaluate the ability to discriminate between normal and keratoconic corneas by analyzing intereye corneal asymmetry parameters and defining a score of similarity that outlines the normal range of asymmetry between right and left eyes.

METHODS

This prospective, non-randomized study included 102 normal corneas of 51 patients and 64 keratoconic corneas of 32 patients. Topographic and tomographic parameters of the right and left eyes were extracted from an elevation and Placido-based corneal topography. Asymmetry was determined by subtracting the right eye value from the left eye value for each variable and by considering the absolute value of the result. A discriminant function was constructed to separate the normal and keratoconic groups.

RESULTS

The mean intereye asymmetry differences were statistically significant for all variables except the vertical and horizontal decentration of the thinnest point, the corneal thickness at 2.5, 3, 3.5, and 4 mm from the thinnest point, and the posterior keratometry at 4.5 mm from the thinnest point. The discriminant function was composed of three variables (the difference between flat and steep keratometry, the 3-mm irregularity, and the anterior elevation of the thinnest point) and reached an area under the receiver operator characteristic curve of 0.992, a sensitivity of 94%, and a specificity of 100%.

CONCLUSIONS

A discriminant function constructed from the intereye difference of three corneal indices may be accurate and useful for the topography-based detection of advanced keratoconus. In the future, incorporating such data in automated artificial intelligence software may improve the detection ability of earlier forms of keratoconus.

摘要

目的

通过分析双眼角膜不对称参数并定义一个相似性评分来评估区分正常角膜和圆锥角膜的能力,该评分勾勒出左右眼之间不对称的正常范围。

方法

这项前瞻性、非随机研究纳入了51例患者的102只正常角膜和32例患者的64只圆锥角膜。从基于海拔高度和普拉西多的角膜地形图中提取左右眼的地形和断层扫描参数。通过从每个变量的左眼值中减去右眼值并考虑结果的绝对值来确定不对称性。构建一个判别函数以区分正常组和圆锥角膜组。

结果

除了最薄点的垂直和水平偏心、距最薄点2.5、3、3.5和4毫米处的角膜厚度以及距最薄点4.5毫米处的后角膜曲率外,所有变量的双眼不对称平均差异均具有统计学意义。判别函数由三个变量组成(平坦和陡峭角膜曲率之间的差异、3毫米不规则度以及最薄点的前表面高度),受试者操作特征曲线下面积达到0.992,灵敏度为94%,特异性为100%。

结论

由三个角膜指数的双眼差异构建的判别函数对于基于地形图的晚期圆锥角膜检测可能是准确且有用的。未来,将此类数据纳入自动化人工智能软件可能会提高早期圆锥角膜的检测能力。

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