SMOTE-Enhanced Explainable Artificial Intelligence Model for Predicting Visual Field Progression in Myopic Normal...

The AI model, enhanced by SMOTE to balance data classes, accurately predicted visual field deterioration in patients with myopic normal tension glaucoma. Using SHAP analysis, the key variables driving disease progression were identified.

To develop and validate a Synthetic Minority Over-sampling Technique (SMOTE)-enhanced artificial intelligence (AI) model for predicting visual field progression in myopic normal tension glaucoma (NTG) patients.

This retrospective cohort study included 100 eyes from myopic NTG patients with a mean follow-up of 10.3±3.2 years. Baseline parameters included intraocular pressure (IOP), central corneal thickness, axial length, and visual field metrics. A SMOTE-enhanced AI model was created to address class imbalance in progression events. Model performance was evaluated using receiver operating characteristic (ROC) analysis, cross-validation, and calibration plots. Predictive factor importance was evaluated through SHapley Additive exPlanations (SHAP) analysis.

Visual field progression was observed in 28% of patients, with a median progression time of 3.2 years. The AI model achieved an area under the ROC curve (AUC) of 0.83 (95% CI, 0.75-0.91), with promising sensitivity (0.81) and specificity (0.77). SHAP analysis identified baseline mean deviation (MD), age, axial length, baseline IOP, and visual field index (VFI) as key predictors. When patients were stratified based on model-predicted risk scores, those with scores above 0.8 had significantly higher observed progression rates (82.6%) compared with those with lower risk scores. Subgroup analysis revealed strong correlations between progression risks and older age, greater axial length, and worse baseline MD.

The SMOTE-enhanced AI model shows reasonable predictive performance and potential clinical utility for identifying visual field progression in myopic NTG patients, though further validation in larger cohorts is needed. By addressing class imbalance and myopia-specific challenges, this approach enables personalized risk stratification and early intervention.