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. 2025 May 22:13:1608494.
doi: 10.3389/fcell.2025.1608494. eCollection 2025.

Machine learning-driven prediction of cycloplegic refractive error in Chinese children

Bichi Chen  1 , Li Tian  2 , Fuyue Tian  2 , Qiaochu Yang  2 , Ying Ruan  3 , Ying Li  1 , Min Cao  2 , Chuanyan Wu  2 , Maoyuan Yang  2 , Suzhong Xu  2 , Ruzhi Deng  2
Affiliations

Machine learning-driven prediction of cycloplegic refractive error in Chinese children

Bichi Chen et al. Front Cell Dev Biol. .

Abstract

Objective: To develop and validate machine learning (ML) models for predicting cycloplegic spherical equivalent refraction (SER) using non-cycloplegic parameters, addressing challenges in pediatric ophthalmic assessments.

Methods: A prospective cohort of 2,274 Chinese children (4,548 eyes) aged 3∼16 years was stratified into development (n = 1819) and validation (n = 455) datasets. Six ML models (linear regression, random forest, extreme gradient boosting, multilayer perceptron, support vector machine, and light gradient boosting machine) were trained on demographics, non-cycloplegic refractive error, and ocular biometrics. Model performance was evaluated using R 2 , mean error (ME), mean absolute error (MAE), and clinical accuracy (proportions within ±0.50 D/±1.00 D).

Results: In the validation dataset, ML models predicted cycloplegic SER with high R 2 (0.920∼0.934), low ME (-0.004∼0.015 D) and MAE (0.385∼0.413 D). The multilayer perceptron model achieved the highest accuracy (R 2 = 0.934, MAE = 0.385 D), with 73.08% and 94.29% of predictions within ±0.50 D and ±1.00 D, respectively. Performance was optimal in children aged 7∼10 years (77.17∼79.70% within ±0.50 D) and those with low myopia (-3.00 to -0.50 D; 83.09∼83.56% within ±0.50 D). Non-cycloplegic measurements systematically overestimated myopia (mean difference: -0.39 ± 0.71 D, P < 0.001), particularly in younger children and hyperopic eyes.

Conclusion: ML models provide accurate estimates of cycloplegic SER using non-cycloplegic parameters, offering a practical alternative for pediatric refractive assessments when cycloplegia is infeasible.

Keywords: cycloplegic refraction; machine learning; myopia; prediction; refractive error.

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Conflict of interest statement

Authors BC, YL were employed by Vision X Medical Technology Co., Ltd. and RD serves as a medical consultant for the same entity. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Differences between non-cycloplegic versus cycloplegic refraction based on age (A), refractive error (B), axial length (C).
FIGURE 2
FIGURE 2
The scatter plot for the predicted versus measured cycloplegic spherical equivalent based on the MLP model. The scatter plot in the development dataset ((A) 3,638 eyes from 1819 children) and in the validation dataset ((B) 910 eyes from 455 children).
See this image and copyright information in PMC

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