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New research · Ophthalmology
Acta ophthalmologica · 2d
AI / informaticsActa ophthalmologica · 2026

Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta-analysis.

Athanasia Sandali, Anna Nikolaidou, Theodora Gianni … Eirini Maliagkani
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OphthalmologyAI / informatics

Artificial intelligence models predict children's future eyeglass prescription with moderate accuracy.

Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta-analysis.

Athanasia Sandali … Eirini Maliagkani
Acta ophthalmologica · 2026
Purpose

To evaluate the prediction accuracy of the included studies, a meta-analysis was performed.

Methods

This systematic review and meta-analysis evaluates the performance of artificial intelligence (artificial intelligence)-based models for predicting the onset and progression of refractive error (refractive error) in children and adolescents and quantitatively synthesizes their prediction accuracy.

0.50
Results
0.50
D
how far off predictions typically were from kids' actual eyeglass strength - lower is better
More results

Eligible studies involved children and adolescents (≤18 years) and applied artificial intelligence-based technologies to predict the onset or progression of refractive error, using non-image-based input data.

Between-study heterogeneity was substantial (τ 2 = 0.0695, I 2 = 100%), indicating major differences across models.

More results

Across all iterations, RMSE estimates ranged narrowly from 0.48 to 0.52 D, with confidence intervals overlapping the main analysis.

The studies' quality assessment showed mostly low concern, apart from the analysis domain, in which 43.8% of studies were rated high risk.

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Conclusion

The study was preregistered in

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