Research progress on the development of myopia prediction models and their predictive performance

Abstract

The incidence of myopia is continuously increasing worldwide, especially in China, where the prevention and control of myopia is of great significance. Accurate prediction of myopia and early intervention are of great importance for slowing down the development of myopia and reducing the social burden. This article reviews the research progress of myopia prediction models and their predictive performance in recent years. It includes models based on refractive power and ocular biological characteristics, such as those using parameters such as refractive power, axial length, and percentile curves for prediction; models combined with environmental factors, which explore the relationship between myopia in school-age children and the age of onset, parents' myopia, education, and various living environmental factors; models based on genetic factors, which predict by evaluating parents' myopia status and single nucleotide polymorphisms discovered in genome-wide association studies. In addition, the article also introduces the application of artificial intelligence in myopia prediction, such as machine learning and deep learning algorithms that can predict changes in axial length growth and refractive power. These research advances provide a reference for establishing more accurate myopia prediction models and help achieve precise and personalized myopia prevention and control.