Abstract
The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. This study aimed to set up and validate a deep learning algorithm to predict corneal back surface power from the corneal front surface power and biometric measures. This study was based on a large dataset of IOLMaster 700measurements from two clinical centres. N=19,553measurements of 19,553 eyes with valid corneal front (CFSPM) and back surface power (CBSPM) data and other biometric measures. After a vector decomposition of CFSPM and CBSPM into equivalent power and projections of astigmatism to the 0°/90° and 45°/135° axes, a multi-output feedforward neural network was derived to predict vector components of CBSPM from CFSPM and other measurements. The predictions were compared with a multivariate linear regression model based on CFSPM components only. After pre-conditioning, a network with two hidden layers each having 12 neurons was derived. The dataset was split into training (70%), validation (15%) and test (15%) subsets. The prediction error (predicted corneal back surface power CBSPP - CBSPM) of the network after training and crossvalidation showed no systematic offset, narrower distributions for CBSPP - CBSPM and no trend error of CBSPP - CBSPM vs. CBSPM for any of the vector components. The multivariate linear model also showed no systematic offset, but broader distributions of the prediction error components and a systematic trend of all vector components vs. CFSPM components. The neural network approach based on CFSPM vector components and other biometric measures outperforms the multivariate linear model in predicting corneal back surface power vector components. Modern biometers can supply all parameters required for this algorithm, enabling reliable predictions for corneal back surface data where direct corneal back surface data are unavailable.
Highlights
The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation
In the context of toric lens calculation, it is widely discussed that the corneal back surface curvature does not follow a fixed ratio of anterior to posterior curvature, as assumed by keratometers which measure corneal front surface curvature and convert this to corneal power using an assumed refractive index.3,4,9–12 The corneal back surface astigmatism shows some against the rule astigmatism which has to be considered during toric lens power calculations in order to yield the correct refractive results after surgery.[13]
The deep learning model derived here could be used in the clinical setting to predict corneal back surface power vector components from the power vector components of the corneal front surface measured with keratometry, corneal topography or a plugin-in keratometer in a biometer together with biometric data such as axial length, central corneal thickness, horizontal corneal diameter available from newer generation biometer models and the patient's age
Summary
The corneal back surface is known to add some against the rule astigmatism, with implications in cataract surgery with toric lens implantation. Over the last two decades, research has investigated the effect of corneal back surface power on the imaging properties of the eye.[1,2] Especially in biometry and intraocular lens power calculation before cataract surgery, the corneal back surface is widely discussed as a relevant parameter, as well as after keratorefractive surgery.3–5 Modern anterior segment tomographers provide reliable data on the corneal front and back surface curvature1,6–8 and central corneal thickness (CCT) as well as the dimensions of the anterior chamber and crystalline lens which is used in several newer generation lens power calculation strategies Most biometers such as the IOLMaster 500 (Carl- Zeiss-Meditec, zeiss.com), the LenStar 900 (Haag-Streit, haag-streit.com) or the OA-2000 (Tomey, tomey.com) do not provide measurements of the corneal back surface. All decompose the astigmatism into vector components using trigonometric functions (e.g., Humphrey notation20,21) and overlay fixed or regression-based offsets to the components to reduce or eliminate the overall trend error
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