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A Mixture-of-Experts Network for Infectious Keratitis Classification Using Multimodal Slit-Lamp Images: A Multicenter
Fen-Fen Li1, Gao-Xiang Li2, Xin-Xin Yu1
1National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, People's Republic of China.
Translational Vision Science & Technology
|June 25, 2026
Summary
KeraFusionNet, a new deep learning model, accurately diagnoses infectious keratitis (IK) subtypes by fusing multiple eye imaging types. This multimodal approach improves automated diagnosis for better clinical decisions.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Infectious keratitis (IK) is a major cause of blindness globally.
- Distinguishing IK subtypes is clinically challenging.
- Current deep learning (DL) methods often use single imaging modalities, missing crucial diagnostic information.
Purpose of the Study:
- To develop a novel DL framework, KeraFusionNet, for accurate IK diagnosis and subtype differentiation.
- To integrate multiple slit-lamp imaging (SLI) modalities for enhanced diagnostic capabilities.
- To overcome the limitations of single-modality DL approaches in IK diagnosis.
Main Methods:
- KeraFusionNet processes three imaging modalities: diffuse white light, slit beam, and cobalt blue light with fluorescein staining.
- The framework employs modality-specific expert subnetworks.
- A dynamic gating network adaptively fuses feature representations for classification.
- Validation was performed on multicenter datasets from Zhejiang Eye Hospital (ZEH) and Aier Guangming Eye Hospital (AGEH).
Main Results:
- KeraFusionNet achieved 87.40% overall accuracy on the ZEH dataset.
- AUCs reached 0.9899 for healthy cornea, 0.9177 for herpes simplex keratitis (HSK), 0.9653 for bacterial keratitis (BK), 0.9803 for fungal keratitis (FK), and 0.9635 for other abnormalities.
- The model demonstrated 83.49% accuracy on the independent AGEH dataset.
Conclusions:
- KeraFusionNet effectively integrates complementary diagnostic information from routine SLI modalities.
- The multimodal fusion framework shows effectiveness and generalizability for automated IK diagnosis.
- This approach offers a promising tool for precise clinical decision-making in ophthalmic practice.
