Abstract

The corneal back surface is known to add some astigmatism against-the-rule, which has to be considered in cataract surgery with toric lens implantation. The purpose of this study was to set up a deep learning algorithm which predicts the total corneal power from keratometry and biometric measures. Based on a large data set of measurements with the IOLMaster 700 from two clinical centres, data from N = 21 108 eyes were included, each record containing valid data for keratometry K, total keratometry TK, axial length AL, central corneal thickness CCT, anterior chamber depth ACD, lens thickness LT and horizontal corneal diameter W2W from an individual eye. After a vector decomposition of K and TK into equivalent power (.EQ) and projections of astigmatism to the 0°/90° (.AST0° ) and 45°/135° (.AST45° ) axis, a multi-output feedforward shallow neural network was derived to predict TK from K, AL, CCT, ACD, LT, W2W and patient age. After some trial and error, the neural network having a Levenberg-Marquardt training function and three hidden layers (10/8/5 neurons) performed best and showed a fast convergence. The data set was split into training data (70%), validation data (15%) and test data (15%). The prediction error (predicted corneal power CPpred minus TK) of the network trained with the training and cross-validated with test data showed systematically narrower distributions for CPEQ-TKEQ, CPAST0° -TKAST0° and CPAST45° -TKAST45° compared with KEQ-TKEQ, KAST0° -TKAST0° and KAST45° -TKAST45° . There was no systematic offset in the components between CPpred and TK. Unlike any fixed correction term, which can compensate only for a static intercept of the astigmatic components TKEQ, TKAST0° and TKAST45° compared with KEQ, KAST0° and KAST45° , our trained neural network was able to reduce the variance in the prediction error significantly. This neural network could be used to account for the corneal back surface astigmatism for biometers where the corneal back surface measurement or total keratometry is not available.

Highlights

  • The corneal back surface is known to add some astigmatism against-the-rule, which has to be considered in cataract surgery with toric lens implantation

  • Missing data or data with a ‘Failed’ or ‘Warning’ in the quality check for keratometry, axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT), W2W, date of birth or examination date provided by the IOLMaster 700 software were excluded, and after checking for ‘Successful’ measurement for corneal back surface power and total corneal power, a data set containing records of measurements from N = 21 108 eyes was used for training, validation and test of our neural network

  • The mean, standard deviation, median, minimum and maximum of the prediction error (CPpred-total keratometry (TK)) and for comparison the difference K-TK are shown for the equivalent power (CPEQTKEQ and KEQ-TKEQ) as well as for the projections of the astigmatism to the 0°/180° axis (CPAST0°-TKAST0° and KAST0°-TKAST0°) and the 45°/ 135° axis (CPAST45°-TKAST45° and KAST45°-TKAST45°), respectively

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Summary

Introduction

The corneal back surface is known to add some astigmatism against-the-rule, which has to be considered in cataract surgery with toric lens implantation. Conclusion: Unlike any fixed correction term, which can compensate only for a static intercept of the astigmatic components TKEQ, TKAST0° and TKAST45° compared with KEQ, KAST0° and KAST45°, our trained neural network was able to reduce the variance in the prediction error significantly. This neural network could be used to account for the corneal back surface astigmatism for biometers where the corneal back surface measurement or total keratometry is not available. With the generations of optical biometers, mostly based on low coherence reflectometry or optical coherence tomography (OCT), it was possible to derive additional measures such as the CCT and LT, and the ACD (or aqueous depth as the distance from corneal endothelium to the lens front vertex) is mostly measured with the same technique, which, together with new lens calculation formulae and better formula constant optimization, significantly improved the predictability of the refractive outcome after cataract surgery (Chen et al 2011; Fisus, Hirnschall & Findl 2021; Fisus, Hirnschall et al.2021)

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