To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging data. A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups: normal (50 eyes), with keratoconus (38 eyes) or with subclinical keratoconus (33 eyes). All eyes were imaged with a Scheimpflug camera and UHR-OCT. Corneal morphological features were extracted from the imaging data. A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes. Fisher’s score was used to rank the differentiable power of each feature. The receiver operating characteristic (ROC) curves were calculated to obtain the area under the ROC curves (AUCs). The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus (AUC = 0.93). The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes. The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes. The epithelial features extracted from the OCT images were the most valuable in the discrimination process. This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.

中文翻译：

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机器学习有助于使用 Scheimpflug 和 OCT 成像方式提高亚临床圆锥角膜的诊断能力

根据 Scheimpflug 相机图像和超高分辨率光学相干断层扫描 (UHR-OCT) 成像数据的组合，开发一个使用机器学习分类器的自动分类系统，以区分圆锥角膜患者临床上未受影响的眼睛与正常对照人群。来自 121 名参与者的 121 只眼被 2 位角膜专家分为 3 组：正常（50 眼）、圆锥角膜（38 眼）或亚临床圆锥角膜（33 眼）。所有眼睛都用 Scheimpflug 相机和 UHR-OCT 成像。从成像数据中提取角膜形态特征。使用神经网络来训练基于这些特征的模型，以区分亚临床圆锥角膜的眼睛和正常眼睛。Fisher 的分数用于对每个特征的可微能力进行排名。计算受试者工作特征 (ROC) 曲线以获得 ROC 曲线下面积 (AUC)。开发的分类模型用于结合 Scheimpflug 相机和 UHR-OCT 的所有特征，显着提高了区分正常眼睛和亚临床圆锥角膜眼睛的可区分能力 (AUC = 0.93)。从 UHR-OCT 成像中提取的角膜上皮中每个个体的厚度分布的变化在区分亚临床圆锥角膜和正常眼的眼中排名最高。基于 Scheimpflug 相机数据和 UHR-OCT 成像数据相结合的机器学习自动分类系统在区分亚临床圆锥角膜和正常眼睛方面表现出优异的性能。从 OCT 图像中提取的上皮特征在鉴别过程中是最有价值的。该分类系统具有提高亚临床圆锥角膜的鉴别能力和圆锥角膜筛查效率的潜力。