In order to increase the probability of having a successful cataract post-surgery, the customisation of the haptic design of the intraocular lens (IOL) according to the characteristics of the patient is recommended. In this study, we present two prediction models based on deep neural networks (DNNs). One is capable of predicting the biomechanical stability of any C-loop IOL, whereas the other can predict the haptic design that fits a desired biomechanical response, enabling the selection of the optimal IOL as a function of the IOL diameter compression. The data used to feed the networks has been obtained from a validated finite element model in which multitude of geometries are tested according to the ISO 11979-3 compression test, a standard for the mechanical properties of the IOLs. The biomechanical response model provides a very high accurate response (Pearson’s r = 0.995), whilst the IOL haptic design model shows that several IOL designs can provide the same biomechanical response (Pearson’s r = 0.992). This study might help manufacturers and ophthalmologists both analyse any IOL design and select the best IOL for each patient. In order to facilitate its application, a graphical user interface (GUI) was created to show the potential of deep learning methods in cataract surgery.

为了增加白内障术后成功的概率，建议根据患者的特点定制人工晶状体 (IOL) 的触觉设计。在这项研究中，我们提出了两种基于深度神经网络 (DNN) 的预测模型。一个能够预测任何 C 形环 IOL 的生物力学稳定性，而另一个可以预测符合所需生物力学响应的触觉设计，从而能够根据 IOL 直径压缩来选择最佳 IOL。用于馈送网络的数据是从经过验证的有限元模型中获得的，其中根据 ISO 11979-3 压缩测试（IOL 的机械性能标准）测试了多种几何形状。r = 0.995)，而 IOL 触觉设计模型表明，几种 IOL 设计可以提供相同的生物力学响应（Pearson 的r = 0.992）。这项研究可能有助于制造商和眼科医生分析任何 IOL 设计并为每位患者选择最佳 IOL。为了促进其应用，创建了图形用户界面 (GUI) 以展示深度学习方法在白内障手术中的潜力。