ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026
Effective automatic classification methods via deep learning for multi-type infectious keratitis diagnosis.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Leakage-Aware Visit-Level Benchmarking Reveals Representational Overlap in Deep Learning for Bacterial Versus Fungal Keratitis Classification.Translational vision science & technology · 2026Article
- Artificial intelligence in microbial keratitis.Indian journal of ophthalmology · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundInfectious keratitis (IK) is a leading cause of corneal blindness, typically caused by bacteria, fungi, viruses, or parasites. Prompt diagnosis and treatment are crucial, yet the absence of a gold standard for pathogen identification complicates timely interventions. Corneal cultures can be time-consuming and prone to false positives, highlighting the need for an automated classification system.
methodsFrom March 2018 to November 2023, 1,065 diffuse pattern slit-lamp images were collected to develop a deep learning system. Five models-EfficientNet_B0, EfficientNet_V2_S, ResNet50, Vision Transformer (ViT), and DeepIK-were trained for corneal infection classification. Key evaluation metrics included accuracy, precision, recall, F1-score, weighted Cohen's Kappa, and the Receiver Operating Characteristic (ROC) curve.
resultsThe EfficientNet_B0 model achieved superior performance across all metrics, with an accuracy of 75.2% (95% CI: 69.6% - 80.8%), sensitivity of 74.9% (95% CI: 69.9% - 80.3%), specificity of 93.8% (95% CI: 92.4% - 95.2%), F1-Score of 74.3% (95% CI: 68.7% - 79.8%), Kappa value of 0.689 (95% CI: 0.618-0.759), and AUC of 0.943 (95% CI: 0.920-0.962).
conclusionsThe EfficientNet_B0 model effectively identified normal eyes and four IK types, showcasing the potential of deep learning in diagnosing keratitis infections. Future enhancements with larger datasets could improve accuracy, facilitating timely treatments and better outcomes for patients.
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