ArticleScientific reports2021
Comparisons of deep learning algorithms for diagnosing bacterial keratitis via external eye photographs.
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 21 papers.
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Who cites it
21 citing papers in PubMed.
- The combined use of in vivo confocal microscopy and optical coherence tomography in monitoring corneal infections.Eye (London, England) · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Infectious Keratitis Management: 10-Year Update.Journal of clinical medicine · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- 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
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Application of Deep Learning Algorithms Based on the Multilayer Y0L0v8 Neural Network to Identify Fungal Keratitis.Sovremennye tekhnologii v meditsine · 2024Article
- Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks.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
- Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy.Scientific reports · 2023Article
- A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification.Scientific reports · 2023Article
- Deep Learning Approach in Image Diagnosis ofDiagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Infectious keratitis: A review.Clinical & experimental ophthalmology · 2022Review
- An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.Frontiers in public health · 2022Review
- Deep learning-based classification of infectious keratitis on slit-lamp images.Therapeutic advances in chronic disease · 2022Article
- Advances in artificial intelligence applications for ocular surface diseases diagnosis.Frontiers in cell and developmental biology · 2022Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial keratitis (BK), a painful and fulminant bacterial infection of the cornea, is the most common type of vision-threatening infectious keratitis (IK). A rapid clinical diagnosis by an ophthalmologist may often help prevent BK patients from progression to corneal melting or even perforation, but many rural areas cannot afford an ophthalmologist. Thanks to the rapid development of deep learning (DL) algorithms, artificial intelligence via image could provide an immediate screening and recommendation for patients with red and painful eyes. Therefore, this study aims to elucidate the potentials of different DL algorithms for diagnosing BK via external eye photos. External eye photos of clinically suspected IK were consecutively collected from five referral centers. The candidate DL frameworks, including ResNet50, ResNeXt50, DenseNet121, SE-ResNet50, EfficientNets B0, B1, B2, and B3, were trained to recognize BK from the photo toward the target with the greatest area under the receiver operating characteristic curve (AUROC). Via five-cross validation, EfficientNet B3 showed the most excellent average AUROC, in which the average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was 74, 64, 77, and 61. There was no statistical difference in diagnostic accuracy and AUROC between any two of these DL frameworks. The diagnostic accuracy of these models (ranged from 69 to 72%) is comparable to that of the ophthalmologist (66% to 74%). Therefore, all these models are promising tools for diagnosing BK in first-line medical care units without ophthalmologists.
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