Aims To develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI). Methods Preoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed. Results The AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model. Conclusions A universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as ‘subclinical’ and ‘forme-fruste’. No data are available. Data not available.

中文翻译：

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用于人工智能驱动的早期圆锥角膜诊断的角膜节段断层扫描生物标志物的通用结构

目的 使用人工智能 (AI) 对节段断层扫描（普拉西多和光学相干断层扫描）进行全面的三维分析。方法 使用屈光手术结果稳定且诊断为不对称或双侧圆锥角膜 (KC) 的屈光手术患者的术前影像数据（MS-39，CSO，意大利）。使用 Zernike 多项式 (ZP) 和随机森林 (RF) AI 模型分析曲率、波前像差和厚度分布。对于训练和交叉验证，有健康组 (n=527)、非常不对称的扩张 (VAE；n=144) 和 KC (n=454)。VAE 眼睛是 KC 患者的对侧眼睛，但没有将这些眼睛进一步手动分离为亚临床或 forme-fruste。结果 AI 在曲线下面积 (0.994)、准确率 (95.6%)、召回率 (98. 5%）和健康眼睛的精确度（92.7%）。对于 KC 眼睛，同样的分别为 0.997、99.1%、98.7% 和 99.1%。对于 VAE 眼睛，同样分别为 0.976、95.5%、71.5% 和 91.2%。有趣的是，人工智能将 36 只（亚临床）VAE 眼睛重新分类为健康，尽管这些眼睛与健康眼睛不同。大多数剩余的 VAE（n=104；forme fruste）眼睛保留了它们的分类，并且与 KC 和健康眼睛不同。此外，后表面特征不属于 AI 模型排名最高的变量。结论 开发了一种将分段断层扫描与 ZP 和 AI 相结合的通用架构。它实现了健康眼和 KC 眼的出色分类。AI 有效地将 VAE 眼睛分类为“亚临床”和“形式沮丧”。没有可用数据。数据不可用。