ArticleDiagnostics (Basel, Switzerland)2021
Using Slit-Lamp Images for Deep Learning-Based Identification of Bacterial and Fungal Keratitis: Model Development and Validation with Different Convolutional Neural Networks.
Article in Diagnostics (Basel, Switzerland), 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.
- Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.Frontiers in public health · 2023Pooled it
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- The combined use of in vivo confocal microscopy and optical coherence tomography in monitoring corneal infections.Eye (London, England) · 2026Article
- Article
- Leakage-Aware Visit-Level Benchmarking Reveals Representational Overlap in Deep Learning for Bacterial Versus Fungal Keratitis Classification.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
- Deep Learning-Based Classification of Slit-Lamp Photograph Quality in Microbial Keratitis.Ophthalmology science · 2026Article
- Bridging Gaps in Fungal Keratitis Management: Novel Diagnostics, Drug Delivery Systems, and Gene Therapies.AAPS PharmSciTech · 2026Review
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment.Scientific data · 2025Article
- Diagnosis of microbial keratitis using smartphone-captured images; a deep-learning model.Journal of ophthalmic inflammation and infection · 2025Article
- A Convolutional Neural Network Using Anterior Segment Photos for Infectious Keratitis Identification.Clinical ophthalmology (Auckland, N.Z.) · 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
- Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study.NPJ digital medicine · 2024Article
- Analysis of the performance of the CorneAI for iOS in the classification of corneal diseases and cataracts based on journal photographs.Scientific reports · 2024Article
- Application of Deep Learning Algorithms Based on the Multilayer Y0L0v8 Neural Network to Identify Fungal Keratitis.Sovremennye tekhnologii v meditsine · 2024Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.Scientific reports · 2023Article
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9 authors.
Funding
Abstract
In this study, we aimed to develop a deep learning model for identifying bacterial keratitis (BK) and fungal keratitis (FK) by using slit-lamp images. We retrospectively collected slit-lamp images of patients with culture-proven microbial keratitis between 1 January 2010 and 31 December 2019 from two medical centers in Taiwan. We constructed a deep learning algorithm consisting of a segmentation model for cropping cornea images and a classification model that applies different convolutional neural networks (CNNs) to differentiate between FK and BK. The CNNs included DenseNet121, DenseNet161, DenseNet169, DenseNet201, EfficientNetB3, InceptionV3, ResNet101, and ResNet50. The model performance was evaluated and presented as the area under the curve (AUC) of the receiver operating characteristic curves. A gradient-weighted class activation mapping technique was used to plot the heat map of the model. By using 1330 images from 580 patients, the deep learning algorithm achieved the highest average accuracy of 80.0%. Using different CNNs, the diagnostic accuracy for BK ranged from 79.6% to 95.9%, and that for FK ranged from 26.3% to 65.8%. The CNN of DenseNet161 showed the best model performance, with an AUC of 0.85 for both BK and FK. The heat maps revealed that the model was able to identify the corneal infiltrations. The model showed a better diagnostic accuracy than the previously reported diagnostic performance of both general ophthalmologists and corneal specialists.
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