Purpose

To evaluate in a sample of normal and keratoconic eyes a simple Bayesian network classifier for keratoconus identification that uses previously developed topographic indices, calculated directly from the digital analysis of the Placido ring images.

Methods

A comparative study was performed on a total of 60 eyes from 60 patients (age 20–60 years) from the Department of keratoconus of INVISION Ophthalmology clinic (Almería, Spain). Patients were divided into two groups depending on their preliminary diagnosis based on the classical topographic criteria: a control group without topographic alteration (30 eyes) and a keratoconus group (30 eyes). The keratoconus group included all grades except grade IV with excessively distorted corneal topography. All cases were examined using the CSO topography system (CSO, Firenze, Italy), and primary corneal Placido-indices were computed, as described in literature. Finally, a classifier was built by fitting a conditional linear Gaussian Bayesian network to the data, using the 5- and 10-fold cross-validation. For comparison, the original data were perturbed with random white noise of different magnitude.

Results

The naïve Bayes classifier showed perfect discrimination ability among normal and keratoconic corneas, with 100% of sensibility and specificity, even in the presence of a very significant noise.

Conclusions

The Bayesian network classifiers are highly accurate and proved a stable screening method to assist ophthalmologists with the detection of keratoconus, even in the presence of noise or incomplete data. This algorithm is easily implemented for any Placido topographic system.

中文翻译：

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使用贝叶斯网络分类器对基于 Placido 的角膜指数进行稳健的圆锥角膜检测。

目的

为了在正常和圆锥角膜样本中评估一个简单的用于圆锥角膜识别的贝叶斯网络分类器，该分类器使用先前开发的地形指数，直接从 Placido 环图像的数字分析中计算出来。

方法

对来自 INVISION 眼科诊所（西班牙阿尔梅利亚）圆锥角膜科的 60 名患者（20-60 岁）的 60 只眼睛进行了比较研究。根据基于经典地形标准的初步诊断，将患者分为两组：没有地形改变的对照组（30只眼）和圆锥角膜组（30只眼）。圆锥角膜组包括除 IV 级外的所有等级，角膜地形过度扭曲。所有病例均使用 CSO 地形系统（CSO，佛罗伦萨，意大利）进行检查，并如文献中所述计算初级角膜 Placido 指数。最后，通过使用 5 倍和 10 倍交叉验证将条件线性高斯贝叶斯网络拟合到数据来构建分类器。为了比较，

结果

朴素贝叶斯分类器在正常和圆锥角膜之间表现出完美的区分能力，具有 100% 的敏感性和特异性，即使存在非常显着的噪音。

结论

贝叶斯网络分类器高度准确，并被证明是一种稳定的筛选方法，即使在存在噪声或不完整数据的情况下，也能帮助眼科医生检测圆锥角膜。该算法可轻松用于任何 Placido 地形系统。