ArticleJournal of ophthalmic inflammation and infection2025
Diagnosis of microbial keratitis using smartphone-captured images; a deep-learning model.
Article in Journal of ophthalmic inflammation and infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
7 citing papers in PubMed.
- A Mixture-of-Experts Network for Infectious Keratitis Classification Using Multimodal Slit-Lamp Images: A Multicenter Study.Translational vision science & technology · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- AI-MK: artificial intelligence for assessing and monitoring microbial keratitis.BMJ open ophthalmology · 2026Observational
- Artificial intelligence in microbial keratitis.Indian journal of ophthalmology · 2026Article
- Fungal recognition in vaginal discharge using deep learning analysis of mobile device-acquired microscopic images.Frontiers in cellular and infection microbiology · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicrobial keratitis (MK) poses a substantial threat to vision and is the leading cause of corneal blindness. The outcome of MK is heavily reliant on immediate treatment following an accurate diagnosis. The current diagnostics are often hindered by the difficulties faced in low and middle-income countries where there may be a lack of access to ophthalmic units with clinical experts and standardized investigating equipment. Hence, it is crucial to develop new and expeditious diagnostic approaches. This study explores the application of deep learning (DL) in diagnosing and differentiating subtypes of MK using smartphone-captured images. MATERIALS AND
methodsThe dataset comprised 889 cases of bacterial keratitis (BK), fungal keratitis (FK), and acanthamoeba keratitis (AK) collected from 2020 to 2023. A convolutional neural network-based model was developed and trained for classification.
resultsThe study demonstrates the model's overall classification accuracy of 83.8%, with specific accuracies for AK, BK, and FK at 81.2%, 82.3%, and 86.6%, respectively, with an AUC of 0.92 for the ROC curves.
conclusionThe model exhibits practicality, especially with the ease of image acquisition using smartphones, making it applicable in diverse settings.
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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 Socratic graph.