Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation

Abstract

This work proposes a new approach based on Machine Learning to predict astigmatism in patients with kera-toconus (KC) after ring implantation. KC is a non-inflamatory, progressive thinning disorder of the cornea, resulting in a protusion, myopia and irregular astigmatism. The intracorneal ring implantation surgery has become a suitable technique to deal with keratoconus without the need of a corneal transplant. Two machine learning (ML) classifiers based on artificial neural network and a decision tree were used in this work. Artificial neural networks performed better than decision trees, achieving an absolute mean error lower than 2 diopters in a validation data set. An analysis of the most relevant features was also carried out.