用三光照照片+临床数据，自动区分真菌与细菌性角膜炎。
Triple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography
- 三阶段融合蓝光、散射、白光照片与病历数据
- 跨中心1645例患者测试，准确率达85.84%
- 对多中心数据偏差敏感，适合临床部署前验证
微生物性角膜炎需快速识别病原体以指导治疗，但培养和PCR检测耗时且资源密集。我们提出一种三阶段多模态框架，利用蓝光、角膜散射和白光照明下的裂隙灯照片及临床元数据，进行细菌性与真菌性角膜炎分类。模型结合跨模态对比学习、模态特异性微调和特征级多模态集成学习，实现患者级预测。在来自印度和美国的多中心数据集（1,645名患者，17,158张图像）上评估，模型达到85.84%准确率、84.46%平均F1分数和0.885 AUC。站点特异性评估显示，合并结果过于乐观，而重采样与平衡再评估提供了更真实的跨站点泛化评估。所有设置下，本框架均为最优表现。代码已开源（https://github.com/yqwang01/TPMKA），数据获取需经密歇根大学数据共享审批。
原文摘要 · Abstract (English)
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. The code is available at https://github.com/yqwang01/TPMKA and dataset access will be provided subject to University of Michigan data-sharing clearance.
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