Triple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography
It addresses the need for rapid, non-culture-based pathogen identification in microbial keratitis, a clinical problem in ophthalmology.
The paper presents a multimodal framework for classifying bacterial vs. fungal keratitis from slit-lamp photographs and clinical metadata, achieving 85.84% accuracy, 84.46% average F1-score, and 0.885 AUC on a multicenter dataset of 1,645 patients.
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.