Triple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography

Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive.

We developed a triple-phase multimodal framework for bacterial-versus-fungal keratitis classification using slit-lamp photographs acquired under blue-light, sclerotic-scatter, and white-light illumination, together with clinical metadata.

The model combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction.

We evaluated the framework on a multicenter dataset of 1,645 patients and 17,158 images from India and the United States.

The model achieved 85.84% accuracy, 84.46% average F1-score, and 0.885 AUC.

Site-specific evaluation showed that pooled results were overly optimistic, whereas resampling- and balance-based re-evaluation provided a more realistic assessment of cross-site generalization.

Under all settings, our framework remained the top-performing approach.

Upon acceptance, the code will be released and dataset access will be provided subject to University of Michigan data-sharing clearance.