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Related Experiment Video

Updated: Oct 17, 2025

Inducement and Evaluation of a Murine Model of Experimental Myopia

Inducement and Evaluation of a Murine Model of Experimental Myopia

Published on: January 22, 2019

Myopia prediction: a systematic review.

Xiaotong Han¹, Chi Liu^1,2, Yanxian Chen^1,3

¹State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

Eye (London, England)

|October 14, 2021

Summary

This summary is machine-generated.

Accurate myopia prediction can identify children at high risk for early intervention. This review summarizes 30 years of research on myopia prediction models, datasets, and performance to guide future studies.

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Area of Science:

Ophthalmology
Public Health
Genetics

Background:

Myopia (nearsightedness) is a growing global health concern, leading to visual impairment.
Increasing prevalence and incidence worldwide necessitate effective prediction strategies.
Early identification of high-risk individuals is crucial for timely intervention.

Purpose of the Study:

To comprehensively review myopia prediction methods and models.
To analyze datasets used in myopia prediction research.
To evaluate the performance of existing myopia prediction approaches.

Main Methods:

Systematic literature review of myopia prediction studies published over the past 30 years.
Analysis of various data types: refractive, biometric, lifestyle, and genetic data.
Comparative assessment of different myopia prediction model performances.

Main Results:

Diverse myopia prediction models have been developed using various data sources.
Data integration approaches show promise for enhanced prediction accuracy.
Performance metrics vary significantly across different models and datasets.

Conclusions:

A comprehensive understanding of myopia prediction methods is essential.
Future research should focus on robust data integration and model validation.
This review provides a guideline for advancing myopia prediction research and interventions.