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Updated: Jan 9, 2026
Inducement and Evaluation of a Murine Model of Experimental Myopia
Published on: January 22, 2019
Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm
Mingjun Gao1, Yanhua Hou1, Yutong Lu1
1Department of Ophthalmology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Insights
This study found that premyopic children experience rapid myopia progression. Machine learning models accurately predict myopia onset and shift, aiding in early intervention for at-risk children.
Area of Science:
- Ophthalmology
- Pediatric Ophthalmology
- Computational Biology
Background:
- Myopia is a growing global health concern, particularly in school-aged children.
- Early identification of children at risk for myopia progression is crucial for timely intervention.
- Understanding longitudinal changes in ocular parameters is key to predicting myopia development.
Purpose of the Study:
- To investigate longitudinal changes in ocular parameters in premyopic children.
- To develop and validate a machine learning model for predicting myopia onset and shift within one year.
- To identify key predictive factors for myopia progression in this cohort.
Main Methods:
- Prospective cohort study of 320 premyopic children (aged 6-12 years).
- Regular measurements of visual acuity, spherical equivalent (SE), axial length (AL), corneal curvature (CC), and subfoveal choroidal thickness (SFCT).
- Machine learning algorithms employed for prediction, with SHAP analysis for feature interpretation.
Main Results:
- 49.3% of participants developed myopia within one year.
- Significant annual SE progression (-0.695 D) and AL elongation (0.356 mm).
- Machine learning model achieved high accuracy (AUC-ROC 0.963) for myopia onset prediction, with SE, parental myopia, SFCT, and age as key predictors.
Conclusions:
- Premyopic children demonstrate accelerated myopia progression.
- Machine learning models offer a promising approach for predicting myopia onset and progression.
- These predictive models can facilitate risk stratification and targeted prevention strategies.
Purpose:
This study aimed to investigate longitudinal changes in ocular parameters and develop a machine learning-based model for predicting myopia onset and shift within 1 year in school-aged premyopic children.
Methods:
This prospective cohort study enrolled 320 premyopic children aged 6-12 years from the Ophthalmology Clinic of The Second Affiliated Hospital of Dalian Medical University. Uncorrected visual acuity (logMAR), cycloplegic spherical equivalent (SE), axial length (AL), average corneal curvature (CC), and subfoveal choroidal thickness (SFCT) were measured at baseline and 6-month intervals for 12 months. Premyopia was defined as - 0.50 D < SE ≤ + 0.75 D. A multivariable analysis evaluated predictive factors including age, gender, parental myopia, baseline SE, AL, CC, axial length/corneal radius (AL/CR), and SFCT. Machine learning algorithms were used to predict 1-year myopia onset and myopia shift, along with Shapley Additive exPlanations (SHAP) interpretation.
Results:
Among 284 participants (88.8% retention rate), 141 children (49.3%) developed myopia. The cohort exhibited an annual SE progression of -0.695 ± 0.222 D and AL elongation of 0.356 ± 0.122 mm. The AL/CR increased from 2.986 ± 0.061 to 3.029 ± 0.072 (p < 0.001), while SFCT demonstrated a significant reduction of 21.535 ± 9.731 μm (p < 0.001). The optimal model achieved an AUC-ROC of 0.963 (95% CI: 0.930-0.997) for myopia onset prediction, with baseline SE emerging as the most significant predictor, followed by parental myopia, SFCT, and age. Meanwhile, our algorithm also achieved clinically acceptable 1-year predictions of SE.
Conclusion:
Premyopic children exhibited accelerated myopic progression. Our machine learning-based predictive models showed promising performance for myopia onset and myopia shift, providing clinically valuable risk stratification for targeted prevention strategies.
