ReviewJournal of clinical medicine2022
Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Tracer Techniques in Ophthalmology: Ocular Applications and Systemic Connections.Diagnostics (Basel, Switzerland) · 2026Review
- The reproducibility of handheld ERGs recorded with skin electrode and natural pupils.Documenta ophthalmologica. Advances in ophthalmology · 2025Article
- Article
- Eye and Systemic Disease Management Changes After Teleophthalmology Screening in Primary Care: Retrospective Cross-Sectional Pilot Study of 200 Consecutive Patients.JMIR formative research · 2025Article
- Mendelian randomization study implicates inflammaging biomarkers in retinal vasculature, cardiovascular diseases, and longevity.Science advances · 2025Article
- Investigation of early-stage cardiovascular-kidney-metabolic syndrome on retinal structure and function evaluated by optical coherence tomography angiography.Frontiers in medicine · 2025Article
- Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.Theranostics · 2025Review
- Oculomics analysis in multiple sclerosis: Current ophthalmic clinical and imaging biomarkers.Eye (London, England) · 2024Review
- A comparative evaluation of deep learning approaches for ophthalmology.Scientific reports · 2024Review
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.Ophthalmology and therapy · 2024Review
- Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES.BioData mining · 2024Article
- A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images.Frontiers in endocrinology · 2024Article
- Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study.International journal of molecular sciences · 2023Article
- Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomic Features from Multimodal Retinal Images.Ophthalmology scienceArticle
- Genotype Prediction from Retinal Fundus Images Using Deep Learning in Eyes with Age-Related Macular Degeneration.Ophthalmology scienceArticle
- Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The retina is a window to the human body. Oculomics is the study of the correlations between ophthalmic biomarkers and systemic health or disease states. Deep learning (DL) is currently the cutting-edge machine learning technique for medical image analysis, and in recent years, DL techniques have been applied to analyze retinal images in oculomics studies. In this review, we summarized oculomics studies that used DL models to analyze retinal images-most of the published studies to date involved color fundus photographs, while others focused on optical coherence tomography images. These studies showed that some systemic variables, such as age, sex and cardiovascular disease events, could be consistently robustly predicted, while other variables, such as thyroid function and blood cell count, could not be. DL-based oculomics has demonstrated fascinating, "super-human" predictive capabilities in certain contexts, but it remains to be seen how these models will be incorporated into clinical care and whether management decisions influenced by these models will lead to improved clinical outcomes.
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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.