ArticleScientific reports2023
From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Review of the Use of Artificial Intelligence in Ophthalmology Imaging: Approximation to Ocular Histopathology.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Review
- Artificial intelligence-assisted diagnosis and subtype differentiation of infectious keratitis.Eye (London, England) · 2026Article
- 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
- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Diagnostic Performance of Publicly Available Large Language Models in Corneal Diseases: A Comparison with Human Specialists.Diagnostics (Basel, Switzerland) · 2025Article
- Diagnosis of microbial keratitis using smartphone-captured images; a deep-learning model.Journal of ophthalmic inflammation and infection · 2025Article
- Slit Lamp Report Generation and Question Answering: Development and Validation of a Multimodal Transformer Model with Large Language Model Integration.Journal of medical Internet research · 2024Article
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Fungal Keratitis in Northwestern Spain: Epidemiology, Risk Factors and Outcomes.Journal of fungi (Basel, Switzerland) · 2024Article
- Application of Deep Learning Algorithms Based on the Multilayer Y0L0v8 Neural Network to Identify Fungal Keratitis.Sovremennye tekhnologii v meditsine · 2024Article
- Article
- Applications of Computer Vision for Infectious Keratitis: A Systematic Review.Ophthalmology scienceReview
- Clinical strategies to prevent recurrence of Herpes simplex and Herpes zoster following ocular surgery: A comprehensive review with practical guidelines.Survey of ophthalmologyReview
- Emerging diagnostic modalities in microbial keratitis: Beyond culture and smear.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
Infectious keratitis (IK) is a major cause of corneal opacity. IK can be caused by a variety of microorganisms. Typically, fungal ulcers carry the worst prognosis. Fungal cases can be subdivided into filamentous and yeasts, which shows fundamental differences. Delays in diagnosis or initiation of treatment increase the risk of ocular complications. Currently, the diagnosis of IK is mainly based on slit-lamp examination and corneal scrapings. Notably, these diagnostic methods have their drawbacks, including experience-dependency, tissue damage, and time consumption. Artificial intelligence (AI) is designed to mimic and enhance human decision-making. An increasing number of studies have utilized AI in the diagnosis of IK. In this paper, we propose to use AI to diagnose IK (model 1), differentiate between bacterial keratitis and fungal keratitis (model 2), and discriminate the filamentous type from the yeast type of fungal cases (model 3). Overall, 9329 slit-lamp photographs gathered from 977 patients were enrolled in the study. The models exhibited remarkable accuracy, with model 1 achieving 99.3%, model 2 at 84%, and model 3 reaching 77.5%. In conclusion, our study offers valuable support in the early identification of potential fungal and bacterial keratitis cases and helps enable timely management.
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