ReviewFrontiers in cell and developmental biology2023
Artificial intelligence-assisted diagnosis of ocular surface diseases.
Review in Frontiers in cell and developmental biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Association of Deep Learning Imaging Algorithm Measures of Microbial Keratitis With Vision Outcomes.Cornea · 2026Article
- Development of a multimodal large language model for early warning and diagnosis of chronic ocular GVHD.NPJ digital medicine · 2026Article
- Review
- Multicentre Pixel-Level Tear Meniscus Segmentation Dataset with Multimodal Imaging for Dry Eye Diagnosis.Scientific data · 2025Article
- High-resolution optical coherence tomography for screening ocular surface tumors: Historical markers and future directions.World journal of clinical cases · 2025Article
- Classification of ocular surface diseases: Deep learning for distinguishing ocular surface squamous neoplasia from pterygium.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of BI-RADS 4A lesions.Quantitative imaging in medicine and surgery · 2025Article
- Diagnosis of microbial keratitis using smartphone-captured images; a deep-learning model.Journal of ophthalmic inflammation and infection · 2025Article
- Strategies for Early Keratoconus Diagnosis: A Narrative Review of Evaluating Affordable and Effective Detection Techniques.Journal of clinical medicine · 2025Review
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images.Frontiers in cell and developmental biology · 2025Article
- Renaissance of an Emerging Pathogen-Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Ocular surface disease following LASIK and cataract surgery: a review of their interrelated complications.Frontiers in medicine · 2025Review
- From the diagnosis of infectious keratitis to discriminating fungal subtypes; a deep learning-based study.Scientific reports · 2023Article
- Large language models and their impact in ophthalmology.The Lancet. Digital health · 2023Review
- 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
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.
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.