ArticleSaudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society
Applications of artificial intelligence-assisted retinal imaging in systemic diseases: A literature review.
Article in Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed, 13 citations in OpenAlex.
- Review
- Systemic Lupus Erythematosus: Ophthalmological Safety Considerations of Emerging and Conventional Therapeutic Agents.International journal of molecular sciences · 2025Review
- Artificial Intelligence-Based Uveitis Diagnosis Through Retinal Vasculature Analysis: A Paradigm Shift in Ocular Tuberculosis.Ophthalmology and therapy · 2025Article
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Deep Learning and The Retina: A New Frontier in Multiple Sclerosis Diagnosis.Current health sciences journalReview
- Ophthalmology's new horizon: Moving from reactive care to proactive artificial intelligence solutions.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
- Digital Health in Diabetes and Cardiovascular Disease.Endocrine researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The retina is a vulnerable structure that is frequently affected by different systemic conditions. The main mechanisms of systemic retinal damage are either primary insult of neurons of the retina, alterations of the local vasculature, or both. This vulnerability makes the retina an important window that reflects the severity of the preexisting systemic disorders. Therefore, current imaging techniques aim to identify early retinal changes relevant to systemic anomalies to establish anticipated diagnosis and start adequate management. Artificial intelligence (AI) has become among the highly trending technologies in the field of medicine. Its spread continues to extend to different specialties including ophthalmology. Many studies have shown the potential of this technique in assisting the screening of retinal anomalies in the context of systemic disorders. In this review, we performed extensive literature search to identify the most important studies that support the effectiveness of AI/deep learning use for diagnosing systemic disorders through retinal imaging. The utility of these technologies in the field of retina-based diagnosis of systemic conditions is highlighted.
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.