Keratoconus is a progressive eye disease and it should be detected in early stage, to avert probable refractive surgery that could develop ecstasies. In this the authors proposes a new computer aided diagnosis model based on Support Vector Machine (SVM) learning to detect the early stage of keratoconus using the available topographic, pachymetric and aberrometry parameters of patients with keratoconus, subclinical keratoconus and normal corneas. The proposed SVM produces 91.8% accuracy with 94.2% sensitivity, 97.5% specificity for classification of early keratoconus from normal; 100% accuracy with 100%, 100% of sensitivity and specificity respectively for classification of early keratoconus from subclinical keratoconus.

圆锥角膜是一种进行性眼病，应尽早发现，以免可能发生屈曲的屈光手术。在本文中，作者提出了一种新的计算机辅助诊断模型，该模型基于支持向量机（SVM）学习，可使用圆锥形角膜，亚临床圆锥形角膜和正常角膜患者的可用地形，测厚和像差参数来检测圆锥形角膜的早期阶段。拟议的支持向量机产生91.8％的准确度，94.2％的灵敏度，97.5％的早期圆锥角膜正常分类的特异性。从亚临床圆锥角膜对早期圆锥角膜进行分类的准确度分别为100％，敏感性和特异性分别为100％，100％。