Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning
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Annals of Biomedical Engineering (Ó 2020) https://doi.org/10.1007/s10439-020-02636-4
Original Article
Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning I. CABEZA-GIL ,1 I. RI´OS-RUIZ,1 and B. CALVO1,2 1
Arago´n Institute of Engineering Research (i3A), University of Zaragoza, Zaragoza, Spain; and 2Centro de Investigacio´n Biome´dica en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain (Received 29 May 2020; accepted 22 September 2020) Associate Editor Eiji Tanaka oversaw the review of this article.
Abstract—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. Keywords—Deep neural networks, IOLs, Biomechanical stability, Haptic design.
INTRODUCTION Ophthalmology is one of the medical fields with the biggest application of artificial intelligence (AI),13,36 and it still leads its development for the detection, diagnosis and treatment of ocular diseases.1,34
Address correspondence to I. Cabeza-Gil, Arago´n Institute of Engineering Research (i3A), University of Zaragoza, Zaragoza, Spain. Electronic mail: [email protected]
This study presents a new possible application of AI, with the eventual aim of assisting the ophthalmologist in the planning of cataract surgery. In cataract surgery, the lens that has become cloudy is removed and replaced with an intraocular lens (IOL) to restore clear vision. At the same time, any refractive errors of the patient can be corrected with the optical part of the IOL implanted. So far, the applications of AI in cataracts have focused on detecting and classifying the grade of the cataract by using convolutional neural networks (CNNs) to analyse slit-lamp images14 and on calculating the lens focal power, focusing on the optics of the lens.16,32 In our case, our approach is to help the planni
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