GlaBoost: A multimodal Structured Framework for Glaucoma Risk Stratification

Abstract

Early and accurate detection of glaucoma is critical to prevent irreversible vision loss. However, existing methods often rely on unimodal data and lack interpretability, limiting their clinical utility. In this paper, we present GlaBoost, a multimodal gradient boosting framework that integrates structured clinical features, fundus image embeddings, and expert-curated textual descriptions for glaucoma risk prediction. GlaBoost extracts high-level visual representations from retinal fundus photographs using a pretrained convolutional encoder and encodes free-text neuroretinal rim assessments using a transformer-based language model. These heterogeneous signals, combined with manually assessed risk scores and quantitative ophthalmic indicators, are fused into a unified feature space for classification via an enhanced XGBoost model. Experiments conducted on a real-world annotated dataset demonstrate that GlaBoost significantly outperforms baseline models, achieving a validation accuracy of 98.71%. Feature importance analysis reveals clinically consistent patterns, with cup-to-disc ratio, rim pallor, and specific textual embeddings contributing most to model decisions. GlaBoost offers a transparent and scalable solution for interpretable glaucoma diagnosis and can be extended to other ophthalmic disorders.