ArticleBMC medical informatics and decision making2025
Through the eye to the heart: a scoping review of artificial intelligence in retinal imaging for cardiovascular disease assessment.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular diseases (CVDs) remain the leading cause of global mortality, yet scalable, non-invasive screening tools are limited. We systematically evaluate the ability of artificial-intelligence-derived retinal biomarkers to simultaneously quantify multiple CVD phenotypes with clinically meaningful accuracy across diverse populations.
methodsFollowing PRISMA-ScR and Arksey & O’Malley, we searched PubMed, Scopus, Web of Science, and Embase through April 2025, screening 643 records. Twenty-one studies met inclusion; we extracted and synthesized data on algorithm class, imaging modality, external-validation strategies, and fairness metrics.
resultsFrom 643 screened records, 21 studies published between 2018 and 2025 were included; six (29%) were multi-national, with China contributing the largest single-country share (24%). Deep Learning (DL) convolutional neural networks (CNNs) dominated algorithmic approaches (57%), followed by transformer-based or hybrid models (38%). Retinal artificial intelligence (Retinal-AI) achieved area under the receiver operating characteristic curve (AUROC) values of 0.89–0.90 for 10-year atherosclerotic cardiovascular disease (ASCVD) risk estimation, 0.97–0.99 for prevalent coronary artery disease (CAD) detection, and 0.64–0.74 for prediction of incident major adverse cardiac events. Multimodal fusion of fundus imaging with basic clinical variables further improved AUROCs by 0.035–0.12 and increased net reclassification by 12–18% across endpoints. Fewer than 10% of studies shared code or datasets, and two-thirds lacked external validation.
conclusionRetinal-AI provides a single-image, multi-risk screening platform suitable for prospective, multi-ethnic trials. Our review establishes a benchmarked evidence base and FAIR-compliant reporting framework to accelerate regulatory qualification and clinical adoption.
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What Socratic 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 Socratic graph.