Prediction of total corneal power from measured anterior corneal power on the IOLMaster 700 using a feedforward shallow neural network

Langenbucher, Achim; Cayless, Alan; Szentmáry, Nóra; Weisensee, Johannes; Wendelstein, Jascha and Hoffmann, Peter (2021). Prediction of total corneal power from measured anterior corneal power on the IOLMaster 700 using a feedforward shallow neural network. Acta Ophthalmologica (Early access).

DOI: https://doi.org/10.1111/aos.15040

Abstract

Background
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.

Methods
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° (.AST) 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.

Results
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, CPAST‐TKAST and CPAST45°‐TKAST45° compared with KEQ‐TKEQ, KAST‐TKAST and KAST45°‐TKAST45°. There was no systematic offset in the components between CPpred and TK.

Conclusion
Unlike any fixed correction term, which can compensate only for a static intercept of the astigmatic components TKEQ, TKAST and TKAST45° compared with KEQ, KAST 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.

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