ArticleScientific reports2021
Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.Frontiers in public health · 2023Pooled it
- Digital Anterior Segment Photography in Clinical Practice: Mixed Methods Study.JMIR formative research · 2026Article
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence-assisted diagnosis and subtype differentiation of infectious keratitis.Eye (London, England) · 2026Article
- Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- A Mixture-of-Experts Network for Infectious Keratitis Classification Using Multimodal Slit-Lamp Images: A Multicenter Study.Translational vision science & technology · 2026Article
- Pediatric Fungal Keratitis: Predisposing Factors, Clinical Features, and Outcomes During a 24-Year Study.Translational vision science & technology · 2025Article
- Infectious Keratitis Management: 10-Year Update.Journal of clinical medicine · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Bacterial and fungal pathogen profiling in infective keratitis: a multimodal diagnostic approach.International ophthalmology · 2025Article
- Navigating the Challenges of Acanthamoeba Keratitis: Current Trends and Future Directions.Life (Basel, Switzerland) · 2025Review
- The Temporal Distribution and Microbial Spectrum in Infectious Keratitis: A Comprehensive Single-Center Study.Journal of clinical medicine · 2025Article
- Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.Scientific reports · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- The importance of clinical experience in AI-assisted corneal diagnosis: verification using intentional AI misleading.Scientific reports · 2025Article
- SLID: a slit-lamp image dataset for deep learning-based anterior eye anatomical segmentation and multi-lesion detection.Frontiers in digital health · 2025Article
- Acanthamoeba keratitis: from pathophysiology to prevention, a contemporary clinical perspective.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases.The British journal of ophthalmology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophthalmologists. To characterize the "face" of an infected cornea, we have adapted a deep learning architecture used for facial recognition and applied it to determine a probability score for a specific pathogen causing keratitis. To record the diverse features and mitigate the uncertainty, batches of probability scores of 4 serial images taken from many angles or fluorescence staining were learned for score and decision level fusion using a gradient boosting decision tree. A total of 4306 slit-lamp images including 312 images obtained by internet publications on keratitis by bacteria, fungi, acanthamoeba, and herpes simplex virus (HSV) were studied. The created algorithm had a high overall accuracy of diagnosis, e.g., the accuracy/area under the curve for acanthamoeba was 97.9%/0.995, bacteria was 90.7%/0.963, fungi was 95.0%/0.975, and HSV was 92.3%/0.946, by group K-fold validation, and it was robust to even the low resolution web images. We suggest that our hybrid deep learning-based algorithm be used as a simple and accurate method for computer-assisted diagnosis of infectious keratitis.
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Registered trials
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