ArticleTherapeutic advances in chronic disease2022
Deep learning-based classification of infectious keratitis on slit-lamp images.
Article in Therapeutic advances in chronic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.Frontiers in public health · 2023Pooled it
- Article
- A Mixture-of-Experts Network for Infectious Keratitis Classification Using Multimodal Slit-Lamp Images: A Multicenter Study.Translational vision science & technology · 2026Article
- Viral eye: Emerging insights into corneal and ocular surface viral infections.World journal of virology · 2025Review
- Navigating the Challenges of Acanthamoeba Keratitis: Current Trends and Future Directions.Life (Basel, Switzerland) · 2025Review
- Acanthamoeba keratitis: from pathophysiology to prevention, a contemporary clinical perspective.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- SLID: a slit-lamp image dataset for deep learning-based anterior eye anatomical segmentation and multi-lesion detection.Frontiers in digital health · 2025Article
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Establishment of a corneal ulcer prognostic model based on machine learning.Scientific reports · 2024Article
- Herpes simplex keratitis: A brief clinical overview.World journal of virology · 2024Review
- Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- AI-based diagnosis of nuclear cataract from slit-lamp videos.Scientific reports · 2023Article
- New Frontiers inBiology · 2023Review
- AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images.Translational vision science & technology · 2023Article
- Updates in Diagnostic Imaging for Infectious Keratitis: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence and Infectious Keratitis: Where Are We Now?Life (Basel, Switzerland) · 2023Review
- Diagnosis ofDiagnostics (Basel, Switzerland) · 2023Review
- Comparisons of artificial intelligence algorithms in automatic segmentation for fungal keratitis diagnosis by anterior segment images.Frontiers in neuroscience · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Infectious keratitis (IK) is an ocular emergency caused by a variety of microorganisms, including bacteria, fungi, viruses, and parasites. Culture-based methods were the gold standard for diagnosing IK, but difficult biopsy, delaying report, and low positive rate limited their clinical application. Objectives: This study aims to construct a deep-learning-based auxiliary diagnostic model for early IK diagnosis. Design: A retrospective study. Methods: IK patients with pathological diagnosis were enrolled and their slit-lamp photos were collected. Image augmentation, normalization, and histogram equalization were applied, and five image classification networks were implemented and compared. Model blending technique was used to combine the advantages of single model. The performance of combined model was validated by 10-fold cross-validation, receiver operating characteristic curves (ROC), confusion matrix, Gradient-wright class activation mapping (Grad-CAM) visualization, and t-distributed Stochastic Neighbor Embedding (t-SNE). Three experienced cornea specialists were invited and competed with the combined model on making clinical decisions. Results: Overall, 4830 slit-lamp images were collected from patients diagnosed with IK between June 2010 and May 2021, including 1490 (30.8%) bacterial keratitis (BK), 1670 (34.6%) fungal keratitis (FK), 600 (12.4%) herpes simplex keratitis (HSK), and 1070 (22.2%) Conclusion: KeratitisNet demonstrates a good performance on clinical IK diagnosis and classification. Deep learning could provide an auxiliary diagnostic method to help clinicians suspect IK using different corneal manifestations.
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