To compare the dynamic corneal response (DCR) and tomographic parameters of thin normal cornea (TNC) with thinnest corneal thickness (TCT) (≤ 500 µm), forme fruste keratoconus (FFKC) and mild keratoconus (MKC) had their central corneal thickness (CCT) matched by Scheimpflug imaging (Pentacam) and corneal visualization Scheimpflug technology (Corvis ST). CCT were matched in 50 eyes with FFKC, 50 eyes with MKC, and 53 TNC eyes with TCT ≤ 500 µm. The differences in DCR and tomographic parameters among the three groups were compared. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic significance of these parameters. Back propagation (BP) neural network was used to establish the keratoconus diagnosis model. Fifty CCT-matched FFKC eyes, 50 MKC eyes and 50 TNC eyes were included. The age and biomechanically corrected intraocular pressure (bIOP) did not differ significantly among the three groups (all P > 0.05). The index of height asymmetry (IHA) and height decentration (IHD) differed significantly among the three groups (all P < 0.05). IHD also had sufficient strength (area under the ROC curves (AUC) > 0.80) to differentiate FFKC and MKC from TNC eyes. Partial DCR parameters showed significant differences between the MKC and TNC groups, and the deflection amplitude of the first applanation (A1DA) showed a good potential to differentiate (AUC > 0.70) FFKC and MKC from TNC eyes. Diagnosis model by BP neural network showed an accurate diagnostic efficiency of about 91%. The majority of the tomographic and DCR parameters differed among the three groups. The IHD and partial DCR parameters assessed by Corvis ST distinguished FFKC and MKC from TNC when controlled for CCT.

中文翻译：

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正常薄角膜、圆锥形角膜和轻度圆锥角膜生物力学和断层扫描参数的比较

为了比较正常薄角膜 (TNC) 和最薄角膜厚度 (TCT)（≤ 500 µm）的动态角膜反应 (DCR) 和断层扫描参数，圆锥形圆锥角膜 (FFKC) 和轻度圆锥角膜 (MKC) 的中央角膜厚度（ CCT) 与 Scheimpflug 成像 (Pentacam) 和角膜可视化 Scheimpflug 技术 (Corvis ST) 相匹配。CCT 匹配 FFKC 的 50 只眼，MKC 的 50 只眼和 TCT ≤ 500 µm 的 53 只 TNC 眼。比较三组间DCR和断层扫描参数的差异。受试者工作特征（ROC）曲线用于分析这些参数的诊断意义。使用反向传播（BP）神经网络建立圆锥角膜诊断模型。包括 50 只 CCT 匹配的 FFKC 眼、50 只 MKC 眼和 50 只 TNC 眼。三组之间的年龄和生物力学校正眼压（bIOP）无显着差异（均 P > 0.05）。三组间身高不对称指数（IHA）和身高偏心指数（IHD）差异显着（均P < 0.05）。IHD 还具有足够的强度（ROC 曲线下面积 (AUC) > 0.80）以将 FFKC 和 MKC 与 TNC 眼区分开来。部分 DCR 参数显示 MKC 和 TNC 组之间存在显着差异，并且第一次压平（A1DA）的偏转幅度显示出良好的区分（AUC > 0.70）FFKC 和 MKC 与 TNC 眼的潜力。BP神经网络诊断模型显示准确诊断效率约为91%。三组之间的大多数断层扫描和 DCR 参数不同。