Department of Research and Development, VISSUM, Alicante, Spain.
Department of Ophthalmology, Faculty of Medicine, Pamukkale University, Denizli, Turkey.
Acta Ophthalmol. 2020 Dec;98(8):e933-e942. doi: 10.1111/aos.14433. Epub 2020 May 15.
To assess the efficacy of morphogeometric and volumetric characterization of the cornea based on three-dimensional (3-D) modelling in diagnosis of subclinical keratoconus (KC).
Cross-sectional study. Ninety-three eyes with subclinical KC with a best spectacle-corrected distance visual acuity ≥20/20 (grade zero KC according to the RETICS classification) and 109 control eyes were included. Computer-based 3-D corneal morphogeometric model was generated using raw topographic data. Distance-, area- and volume-based parameters were used for statistical analysis. Distance parameters included deviation of anterior (D )/posterior (D ) apices and minimum thickness points (D , D ) from corneal vertex, and D -D difference. Areal variables were derived from anterior (A ) and posterior (A ) corneal surfaces, sagittal plane passing through corneal apices (A , A ) and thinnest point (A , A ). Total corneal volume (V ) and volumetric distribution (with 0.1mm steps) centred to thinnest corneal point (VOL ) and anterior (VOL )/posterior (VOL ) apices comprised the volume-based parameters.
In the subclinical KC group, all D values, D -D difference, A , A and A values were higher (p < 0.001), while A , A , V , VOL , VOL and VOL values were lower when compared to the control group (p < 0.001). Regression analysis-based formula correctly classified 96.8% of the eyes with subclinical KC and 94.5% of the normal ones (p < 0.0001).
Eyes with subclinical KC seem to represent asymmetrically displaced anterior and posterior corneal apex, corneal thinning and volume loss. 3-D morphogeometric and volumetric parameters and differentiation formula can be incorporated into topography software to detect subclinical KC with high sensitivity and specificity in clinical practice.
评估基于三维（3-D）建模的角膜形态和体积特征对亚临床圆锥角膜（KC）的诊断效能。
这是一项横断面研究。纳入了 93 只亚临床 KC 眼（根据 RETICS 分类为 0 级 KC，最佳矫正远距视力≥20/20）和 109 只对照眼。使用原始地形图数据生成基于计算机的 3-D 角膜形态模型。使用距离、面积和体积参数进行统计分析。距离参数包括角膜顶点前（D）/后（D）顶点和最小厚度点（D'、D"）的偏离，以及 D-D 差值。面积变量来自前（A）和后（A）角膜面、穿过角膜顶点的矢状面（A'、A"）和最薄点（A'、A"）。总角膜体积（V）和以最薄角膜点为中心的体积分布（0.1mm 步长）（VOL）和前（VOL）/后（VOL）顶点包含体积参数。
在亚临床 KC 组中，所有 D 值、D-D 差值、A、A 和 A 值均较高（p<0.001），而 A、A、V、VOL、VOL 和 VOL 值均较低（p<0.001）。基于回归分析的公式正确分类了 96.8%的亚临床 KC 眼和 94.5%的正常眼（p<0.0001）。
亚临床 KC 眼似乎表现为前、后角膜顶点不对称移位、角膜变薄和体积丧失。3-D 形态和体积参数以及区分公式可被纳入到地形图软件中，以在临床实践中实现对亚临床 KC 的高敏感性和特异性检测。
