Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate

Clinical Snapshot

Key Findings

• P Value: P > 0.05 for baseline characteristics comparison

• Effect Size: XGBoost model R² = 0.766 on validation set

• Primary Outcome: Axial elongation rate prediction accuracy

• Nnt Or Sensitivity: Pupil size importance score 100, corneal thickness 40.88, age of onset 17.96

• Confidence Interval: Not reported

Clinical Application

Requires standard ocular biometry equipment available in most Australian optometry and ophthalmology practices Could complement existing RACGP myopia management guidelines, though validation in Australian population needed before clinical implementation Myopic patients requiring progression risk assessment, particularly in specialist ophthalmology and optometry settings

Abstract

PURPOSE: This study aimed to develop a machine learning-based prediction model for myopia progression using ocular biometric parameters to provide an objective assessment tool for clinical practice. METHODS: A retrospective analysis was conducted on patients treated at Shanghai Parkway Health Ophthalmology Department as the training set, and myopic individuals from the Optometry Center of Peking University People's Hospital as the validation set. Demographic and biometric data were collected, including central corneal thickness (CCT), axial length (AL), corneal curvature (K-value), anterior chamber depth (ACD), corneal diameter (WTW), and pupil size (PS). Seven machine learning models (e.g., XGBoost, random forest, support vector machine) were employed for modeling, with performance optimized via 5-fold cross-validation. Model accuracy was evaluated using mean squared error (MSE) and the coefficient of determination (R²), and variable importance was analyzed. RESULTS: No statistically significant differences were observed in baseline characteristics between the training and validation sets (all P > 0.05). The XGBoost model demonstrated the best performance, achieving R² = 0.913 (MSE = 0.005) on the training set and R² = 0.766 (MSE = 0.016) on the test set. Variable importance analysis revealed pupil size (score 100) and corneal thickness (40.88) as the key predictors of axial elongation rate, followed by age of onset (17.96). CONCLUSION: The machine learning-based prediction model effectively utilizes ocular biometric data to assess myopia progression risk, with pupil size and corneal thickness identified as core predictive factors. This model provides a quantitative tool for early clinical intervention. Future studies should expand the sample size and incorporate additional biomarkers to optimize performance.

References

1. 1.Zhou, P., Chen, S., Li, Y., & Li, Y. (2026). Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate. PLoS One. https://doi.org/10.1371/journal.pone.0348085