Prediction of refractive error and its progression: a machine learning-based algorithm

Abstract

Objective Myopia is the refractive error that shows the highest prevalence for younger ages in Southeast Asia and its projection over the next decades indicates that this situation will worsen. Nowadays, several management solutions are being applied to help fight its onset and development, nonetheless, the applications of these techniques depend on a clear and reliable assessment of risk to develop myopia. Methods and analysis In this study, population-based data of Chinese children were used to develop a machine learning-based algorithm that enables the risk assessment of myopia’s onset and development. Cross-sectional data of 12 780 kids together with longitudinal data of 226 kids containing age, gender, biometry and refractive parameters were used for the development of the models. Results A combination of support vector regression and Gaussian process regression resulted in the best performing algorithm. The Pearson correlation coefficient between prediction and measured data was 0.77, whereas the bias was −0.05 D and the limits of agreement was 0.85 D (95% CI: −0.91 to 0.80D). Discussion The developed algorithm uses accessible inputs to provide an estimate of refractive development and may serve as guide for the eye care professional to help determine the individual best strategy for management of myopia.