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

M. Valdés-Mas, J. Martín-Guerrero, M. J. Rupérez, C. Peris, Carlos Monserrat Aranda
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引用次数: 8

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.
机器学习预测圆锥角膜患者角膜环植入术后散光
本文提出了一种基于机器学习的预测角膜圆锥(KC)患者环植入术后散光的新方法。KC是一种非炎症性、进行性角膜变薄的疾病,可导致角膜突出、近视和不规则散光。角膜内环植入术已成为治疗圆锥角膜的一种不需要角膜移植的方法。本文采用了基于人工神经网络和决策树的机器学习分类器。人工神经网络的表现优于决策树,在验证数据集中实现了低于2屈光度的绝对平均误差。还对最相关的特征进行了分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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