ReviewEye and vision (London, England)2024
Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.
Review in Eye and vision (London, England), 2024. 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 23 papers, 1 of them a synthesis that pooled it.
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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection and Management of Geographic Atrophy Secondary to Age-Related Macular Degeneration Using Noninvasive Retinal Images and Artificial Intelligence: Systematic Review.Journal of medical Internet research · 2025Pooled it
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Review
- Association of Remnant Cholesterol With the Retinal Capillary Plexus.Translational vision science & technology · 2026Article
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- Study on fundus characteristics of preschool children based on artificial intelligence quantitative technology.BMC ophthalmology · 2026Observational
- Beyond the dataset: integrating public voices in data science.Research involvement and engagement · 2026Article
- Multivariable Prognostic Models for Age-Related Macular Degeneration: A Systematic Review.Journal of ophthalmology · 2026Review
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
- Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).Frontiers in endocrinology · 2026Review
- Artificial intelligence in fundus photography for type 2 diabetes: a scoping review of systemic biomarkers and multi-organ risk prediction.Frontiers in digital health · 2026Review
- Does DeepSeek Provide Clinically Acceptable Intraocular Lens (IOL) Power Predictions in Cataract Surgery? A Proof-of-Concept Study.Journal of clinical medicine · 2025Article
- Trochlear Nerve Palsy: A Systematic Review of Etiologies and Diagnostic Insights.Diagnostics (Basel, Switzerland) · 2025Review
- Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).Biomedical reports · 2025Review
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics.Sensors (Basel, Switzerland) · 2025Article
- Vision transformer-based stratification of pre/diabetic and pre/hypertensive patients from retinal photographs for 3PM applications.The EPMA journal · 2025Article
- AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases.International journal of emergency medicine · 2025Review
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Association between gamma-glutamyl transferase levels and the retinal age gap.Frontiers in physiology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care.
conclusionAI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.
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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 OpenQuestion graph.