Who needs myopia control?

Area of Science:

• Ophthalmology
• Pediatric Health
• Public Health

Background:

• Myopia prevalence is rising in East Asian school-aged children, posing a significant public health challenge.
• Current myopia control strategies like atropine and orthokeratology have limitations, especially for rapidly progressing cases.
• Effective myopia management necessitates improved risk assessment for onset and progression.

Purpose of the Study:

• To highlight the critical need for enhanced risk assessment in pediatric myopia management.
• To explore the potential of individualized prediction models for myopia progression.
• To emphasize the importance of identifying at-risk populations for targeted interventions.

Main Methods:

• Review of current myopia control interventions and their associated risks.
• Discussion of traditional population-based prediction models.
• Exploration of machine learning and age-specific refraction data for individualized prediction.

Main Results:

• High myopia incidence (20%-30%) necessitates effective control strategies.
• Higher atropine concentrations and orthokeratology carry risks of side effects and complications.
• Individualized prediction models show promise for better risk stratification.

Conclusions:

• Accurate risk assessment is crucial for guiding clinical decisions in myopia management.
• Machine learning offers a promising avenue for personalized myopia prediction.
• Identifying children who will benefit most from interventions is key to successful myopia control.