ReviewLife (Basel, Switzerland)2023
Artificial Intelligence and Infectious Keratitis: Where Are We Now?
Review in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Association of Deep Learning Imaging Algorithm Measures of Microbial Keratitis With Vision Outcomes.Cornea · 2026Article
- Beyond the Barrier: Overcoming Ocular Antimicrobial Resistance Through AI and Novel Therapeutics.International journal of molecular sciences · 2026Review
- 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
- An exploration of the ocular mysteries linking nanoparticles to the patho-therapeutic effects against keratitis.Journal of nanobiotechnology · 2025Review
- Diagnosis of microbial keratitis using smartphone-captured images; a deep-learning model.Journal of ophthalmic inflammation and infection · 2025Article
- Article
- Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Application of Metagenomic Next-Generation Sequencing in the Diagnosis of Infectious Keratitis.Journal of ophthalmology · 2024Article
- Fungal Keratitis: Diagnosis, Management, and Recent Advances.Clinical ophthalmology (Auckland, N.Z.) · 2024Review
- From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.Scientific reports · 2023Article
- Exploring the Ocular Transmission Potential of Severe Acute Respiratory Syndrome Coronavirus 2 and the Assessment of Conjunctival Swab Test Results: A Concise Review.Journal of current ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Infectious keratitis (IK), which is one of the most common and catastrophic ophthalmic emergencies, accounts for the leading cause of corneal blindness worldwide. Different pathogens, including bacteria, viruses, fungi, and parasites, can cause IK. The diagnosis and etiology detection of IK pose specific challenges, and delayed or incorrect diagnosis can significantly worsen the outcome. Currently, this process is mainly performed based on slit-lamp findings, corneal smear and culture, tissue biopsy, PCR, and confocal microscopy. However, these diagnostic methods have their drawbacks, including experience dependency, tissue damage, cost, and time consumption. Diagnosis and etiology detection of IK can be especially challenging in rural areas or in countries with limited resources. In recent years, artificial intelligence (AI) has opened new windows in medical fields such as ophthalmology. An increasing number of studies have utilized AI in the diagnosis of anterior segment diseases such as IK. Several studies have demonstrated that AI algorithms can diagnose and detect the etiology of IK accurately and fast, which can be valuable, especially in remote areas and in countries with limited resources. Herein, we provided a comprehensive update on the utility of AI in IK.
Indexed as
Identifiers
What OpenQuestion holds
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.