ArticleOphthalmology science2022
Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks.
Article in Ophthalmology science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.Frontiers in public health · 2023Pooled it
- The combined use of in vivo confocal microscopy and optical coherence tomography in monitoring corneal infections.Eye (London, England) · 2026Article
- Artificial intelligence-assisted diagnosis and subtype differentiation of infectious keratitis.Eye (London, England) · 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
- Article
- A novel hybrid approach for microbial detection using deep ensemble learning.BMC microbiology · 2026Article
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Effective automatic classification methods via deep learning for multi-type infectious keratitis diagnosis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Automated microbial recognition from gram-stained micrographs via CNN and metadata decision support.Frontiers in cellular and infection microbiology · 2026Article
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Infectious Keratitis Management: 10-Year Update.Journal of clinical medicine · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 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
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- 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
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
Purpose: Develop computer vision models for image-based differentiation of bacterial and fungal corneal ulcers and compare their performance against human experts. Design: Cross-sectional comparison of diagnostic performance. Participants: Patients with acute, culture-proven bacterial or fungal keratitis from 4 centers in South India. Methods: Five convolutional neural networks (CNNs) were trained using images from handheld cameras collected from patients with culture-proven corneal ulcers in South India recruited as part of clinical trials conducted between 2006 and 2015. Their performance was evaluated on 2 hold-out test sets (1 single center and 1 multicenter) from South India. Twelve local expert cornea specialists performed remote interpretation of the images in the multicenter test set to enable direct comparison against CNN performance. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) individually and for each group collectively (i.e., CNN ensemble and human ensemble). Results: The best-performing CNN architecture was MobileNet, which attained an AUC of 0.86 on the single-center test set (other CNNs range, 0.68-0.84) and 0.83 on the multicenter test set (other CNNs range, 0.75-0.83). Expert human AUCs on the multicenter test set ranged from 0.42 to 0.79. The CNN ensemble achieved a statistically significantly higher AUC (0.84) than the human ensemble (0.76; Conclusions: Computer vision models achieved superhuman performance in identifying the underlying infectious cause of corneal ulcers compared with cornea specialists. The best-performing model, MobileNet, attained an AUC of 0.83 to 0.86 without any additional clinical or historical information. These findings suggest the potential for future implementation of these models to enable earlier directed antimicrobial therapy in the management of infectious keratitis, which may improve visual outcomes. Additional studies are ongoing to incorporate clinical history and expert opinion into predictive models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.