Department of Mechanical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
Basir Eye Health Research Center, Tehran, Iran.
Curr Pharm Des. 2018;24(37):4474-4483. doi: 10.2174/1381612825666181224123939.
Keratoconus is recognized by asymmetrical thinning and bulging of the cornea, resulting in distortion in the surface of the cornea. Keratoconus also alters the biomechanical properties of the cornea, which can be an indicator of the healthy and keratoconus eyes. This study was aimed at employing a combination of clinical data, finite element method (FEM), and artificial neural network (ANN) to establish a novel biomechanical- based diagnostic method for the keratoconus eyes.
To do that, the clinical-biomechanical parameters of 40 healthy and 40 keratoconus eyes were obtained via the Pentacam and non-contact tonometer (Corvis ST, Oculus Optikgeräte, Wetzlar, Germany) devices. Intraocular pressure (IOP) was measured using a Goldmann applanation tonometer as well as Corvis. According to the geometry of the cornea, the FE model of each cornea was made and the same boundary and loading conditions were applied not only to confirm the FE model in terms of the biomechanical parameters but also to calculate the amount of von Mises stress in the apex of the cornea. The clinical-biomechanical data of the Corvis along with the von Mises stresses were then incorporated into the ANN algorithm to distinguish the healthy and keratoconus corneas on a basis of the resulted von Mises stresses. The proposed programming code, according to the input data from the Corvis, enabled to predict whether the cornea is keratoconus or not. Finally, to verify the results of the proposed method, 155 individuals were examined.
The clinical and biomechanical results of the Corvis revealed that the healthy corneas have a higher thickness compared to the keratoconus ones. No significant differences were observed among the IOPs, 1st applanation length, and pick distance in the highest concavity. The 2nd applanation length and radius in the highest concavity of the healthy cornea were higher than the keratoconus ones. Conversely, the 1st and 2nd applanation velocities and deformation amplitudes of the keratoconus corneas were higher than the healthy ones. The FE results also showed higher stresses for the healthy corneas compared to the keratoconus ones. The ANN was also well verified since it demonstrated more than 95.5% accuracy on diagnosing the keratoconus eyes.
These findings have implications not only for identifying the keratoconus corneas as an important clinical and surgical tool for eye care professionals but also for providing both a quantitative and an accurate approach to the problem of understanding the biomechanical nature of keratoconus.
圆锥角膜的特征是角膜不对称变薄和膨出，导致角膜表面变形。圆锥角膜还改变了角膜的生物力学特性，这可以作为健康和圆锥角膜眼睛的指标。本研究旨在结合临床数据、有限元法（FEM）和人工神经网络（ANN），建立一种新的基于生物力学的圆锥角膜诊断方法。
为此，通过 Pentacam 和非接触眼压计（德国 Oculus Optikgeräte 的 Corvis ST）设备获得 40 只健康眼和 40 只圆锥角膜眼的临床生物力学参数。眼压（IOP）通过 Goldmann 压平眼压计和 Corvis 测量。根据角膜的几何形状，为每个角膜制作了 FE 模型，并施加相同的边界和加载条件，不仅验证了 FE 模型在生物力学参数方面的准确性，还计算了角膜顶点处的 von Mises 应力。然后，将 Corvis 的临床生物力学数据和 von Mises 应力纳入 ANN 算法中，根据所得的 von Mises 应力来区分健康和圆锥角膜的角膜。根据 Corvis 的输入数据，该编程代码能够预测角膜是否为圆锥角膜。最后，为了验证该方法的结果，对 155 人进行了检查。
Corvis 的临床和生物力学结果表明，健康角膜的厚度比圆锥角膜高。在最高凹陷处，眼压、第一次压平长度和最高点距离之间没有显著差异。健康角膜的第二次压平长度和最高点半径高于圆锥角膜。相反，圆锥角膜的第一次和第二次压平速度和变形幅度高于健康角膜。FE 结果还表明，健康角膜的应力高于圆锥角膜。ANN 也得到了很好的验证，因为它在诊断圆锥角膜眼方面的准确率超过 95.5%。
这些发现不仅对识别圆锥角膜角膜具有重要意义，是眼保健专业人员的重要临床和手术工具，而且为理解圆锥角膜的生物力学性质提供了一种定量和准确的方法。
