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

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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.

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Acknowledgments

The authors gratefully acknowledge research support from the Spanish Ministerio de ciencia, innovacion y universidades (Grant DPI2017-84047-R) and the Department of Industry and Innovation (Government of Aragon) through the research group Grant T24-20R (cofinanciado con Feder 2014-2020: Construyendo Europa desde Aragon). The authors also acknowledge the support of the Tissue Characterization Platform of CIBER-BBN, an initiative funded by the VI National R & D & i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. I. Cabeza-Gil and I. Ríos-Ruiz were supported by PRE2018-084021 and Government of Aragon, order IIU/1408/2018, respectively.

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No author has a financial or proprietary interest in any material or method mentioned.

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Authors and Affiliations

  1. Aragón Institute of Engineering Research (i3A), University of Zaragoza, Zaragoza, Spain

    I. Cabeza-Gil, I. Ríos-Ruiz & B. Calvo

  2. Centro de Investigación Biomédica en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain

    B. Calvo

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  1. I. Cabeza-Gil
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  3. B. Calvo
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Correspondence to I. Cabeza-Gil.

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Associate Editor Eiji Tanaka oversaw the review of this article.

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Cabeza-Gil, I., Ríos-Ruiz, I. & Calvo, B. Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning. Ann Biomed Eng 48, 2988–3002 (2020). https://doi.org/10.1007/s10439-020-02636-4

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