Predicting Glaucoma Progression with Artificial Intelligence: A Meta-Analysis of Machine Learning Models

Abstract

Introduction: Glaucoma, a leading cause of irreversible blindness, requires early detection and prediction of progression to preserve vision. Artificial intelligence (AI) offers promising tools for analyzing complex ophthalmic data and identifying high-risk individuals. This meta-analysis evaluates the performance of machine learning (ML) models in predicting glaucoma progression.

Methods: A systematic search of PubMed, Scopus, and Web of Science databases was conducted for studies published between 2013 and 2024 that investigated the use of ML models to predict glaucoma progression. Studies reporting performance metrics like sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and accuracy were included.

Results: Six studies met the inclusion criteria, encompassing 1,250 participants. The pooled sensitivity and specificity of ML models for predicting glaucoma progression were 0.81 (95% CI: 0.78-0.84) and 0.77 (95% CI: 0.73-0.81), respectively. The pooled AUC was 0.88 (95% CI: 0.86-0.90), indicating excellent discriminatory ability.

Conclusion: ML models hold significant potential for predicting glaucoma progression with high accuracy. Further research with larger, more diverse datasets is needed to validate these findings and develop clinically applicable tools.