Myopia prediction: a systematic review

Abstract

Myopia is a leading cause of visual impairment and has raised significant international concern in recent decades with rapidly increasing prevalence and incidence worldwide. Accurate prediction of future myopia risk could help identify high-risk children for early targeted intervention to delay myopia onset or slow myopia progression. Researchers have built and assessed various myopia prediction models based on different datasets, including baseline refraction or biometric data, lifestyle data, genetic data, and data integration. Here, we summarize all related work published in the past 30 years and provide a comprehensive review of myopia prediction methods, datasets, and performance, which could serve as a useful reference and valuable guideline for future research.

Fig. 1. The workflow for myopia prediction.

These five steps indicate the general workflow to develop a myopia prediction model.

Fig. 2. A taxonomy of myopia prediction methods from a data-driven perspective.

Myopia prediction can be generally classified into four categories from a data-driven perspective, and the contents shown in the colored blocks indicate related publications in the literature.