Experimental evaluation of the injection force exerted in intraocular lens delivery with syringe-type injectors.

The process of intraocular lens (IOL) delivery within the capsular bag during cataract surgery is crucial, as the integrity of the IOL, the injector and the ocular structures should be preserved at all times. This study aims to obtain the main parameters that affect the injection force exerted in the ejection of an intraocular lens (IOL) through syringe-type injectors. For that purpose, ejection tests were carried out in vitro, measuring the resistance force throughout the entire delivery process. The effect of IOL material, haptic design, IOL thickest area and ophthalmic viscosurgical device (OVD) was studied by ejecting seven IOLs with four syringe-type injectors of different sizes, 3.0, 2.2 and 1.8 mm. In all injectors, plate hydrophilic IOLs present the lowest resistance forces; hydrated C-loop hydrophobic IOLs present higher forces and the C-loop hydrophobic IOL in dry conditions presents the highest resistance forces. All IOLs could be properly delivered with an injector size of 2.2 mm, making injector sizes of 3.0 mm outdated. The injector size of 1.8 mm damaged several IOLs. IOL material and cartridge nozzle size were the most influential parameters in IOL delivery. IOL thickest area was also relevant but in a lesser extent whereas IOL haptic design was not as relevant.

Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning.

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.