Sideroudi Haris, Labiris Georgios, Georgatzoglou Kimon, Ditzel Fienke, Siganos Charalambos, Kozobolis Vassilios
From the Eye Institute of Thrace (Sideroudi, Georgios, Georgatzoglou, Vassilios), Democritus University, and the Department of Ophthalmology (Georgios, Vassilios), University Hospital of Alexandroupolis, and the Institute of Vision and Optics (Signos), University of Crete, Heraklion, Greece; the Medical Center (Ditzel), University of Groningen, Groningen, the Netherlands.
From the Eye Institute of Thrace (Sideroudi, Georgios, Georgatzoglou, Vassilios), Democritus University, and the Department of Ophthalmology (Georgios, Vassilios), University Hospital of Alexandroupolis, and the Institute of Vision and Optics (Signos), University of Crete, Heraklion, Greece; the Medical Center (Ditzel), University of Groningen, Groningen, the Netherlands.
J Cataract Refract Surg. 2016 May;42(5):731-7. doi: 10.1016/j.jcrs.2016.01.049.
To evaluate the contribution of Fourier analysis of videokeratographic data in the diagnosis of subclinical keratoconus and keratoconus.
Eye Institute of Thrace, Democritus University, Alexandroupolis, Greece.
Observational case series.
The following Pentacam-derived parameters, resulting from Fourier decomposition of keratometric data, were evaluated for their diagnostic capacity using receiver operating curves: spherical component and eccentricity, maximum decentration, regular astigmatism in the center and in the periphery, mean astigmatism, irregularities, regular astigmatism in the center plus the irregularities, and total astigmatism. Logistic regression was performed to identify a combined diagnostic model.
The study comprised 80 keratoconus eyes, 55 eyes diagnosed with subclinical keratoconus, and 50 normal eyes. Significant differences were detected in spherical eccentricity, maximum decentration, irregularities, regular astigmatism in the center and in the periphery, regular astigmatism in the center plus the irregularities, mean astigmatism, and total astigmatism parameters between the groups. Almost all parameters had high diagnostic ability in both study groups (area under the curve >90%). Among individual parameters, those with the highest predictive accuracy were the regular astigmatism in the center plus the irregularities (subclinical keratoconus 97.6%, keratoconus 98.8%) and the maximum decentration (subclinical keratoconus 91.4%, keratoconus 98.5%). Sufficient predictive accuracy (subclinical keratoconus 99.4, keratoconus 100%) was identified in a diagnostic model that combined the regular astigmatism in the center plus the irregularities and the maximum decentration.
Fourier decomposition of keratometric data provided parameters with high accuracy in differentiating corneas with subclinical keratoconus from normal corneas and should be included to allow prompt diagnosis of keratoconus.
None of the authors has a financial or proprietary interest in any material or method mentioned.
评估角膜地形图数据的傅里叶分析在亚临床圆锥角膜和圆锥角膜诊断中的作用。
希腊亚历山德鲁波利斯德谟克利特大学色雷斯眼科学院。
观察性病例系列。
使用受试者工作特征曲线评估以下源自Pentacam的参数（这些参数由角膜曲率数据的傅里叶分解得出）的诊断能力：球面成分和偏心率、最大偏心度、中央和周边的规则散光、平均散光、不规则度、中央规则散光加不规则度以及总散光。进行逻辑回归以确定一个联合诊断模型。
该研究包括80只圆锥角膜眼、55只被诊断为亚临床圆锥角膜的眼以及50只正常眼。在球面偏心率、最大偏心度、不规则度、中央和周边的规则散光、中央规则散光加不规则度、平均散光以及总散光参数方面，各研究组之间存在显著差异。几乎所有参数在两个研究组中均具有较高的诊断能力（曲线下面积>90%）。在各个参数中，预测准确性最高的是中央规则散光加不规则度（亚临床圆锥角膜97.6%，圆锥角膜98.8%）和最大偏心度（亚临床圆锥角膜91.4%，圆锥角膜98.5%）。在一个将中央规则散光加不规则度和最大偏心度相结合的诊断模型中，确定了足够的预测准确性（亚临床圆锥角膜99.4%，圆锥角膜100%）。
角膜曲率数据的傅里叶分解提供了在区分亚临床圆锥角膜角膜与正常角膜方面具有高精度的参数，应纳入这些参数以实现圆锥角膜的早期诊断。
作者均未对文中提及的任何材料或方法拥有财务或专利权益。
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