ArticleJACC. Advances2024
Blood Pressure Predicted From Artificial Intelligence Analysis of Retinal Images Correlates With Future Cardiovascular Events.
Article in JACC. Advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- 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
- Applications of artificial intelligence and computational approaches to imaging for hypertension identification, phenotyping, and outcome prediction: a systematic review.European heart journal. Digital health · 2026Review
- The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026Review
- Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations.Scientific reports · 2026Article
- Pre-trained Artificial Intelligence Models in the Prediction and Classification of Atherosclerotic Cardiovascular Disease.The Eurasian journal of medicine · 2025Article
- Application of Artificial Intelligence to Deliver Healthcare From the Eye.JAMA ophthalmology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: High systolic blood pressure (SBP) is one of the leading modifiable risk factors for premature cardiovascular death. The retinal vasculature exhibits well-documented adaptations to high SBP and these vascular changes are known to correlate with atherosclerotic cardiovascular disease (ASCVD) events. Objectives: The purpose of this study was to determine whether using artificial intelligence (AI) to predict an individual's SBP from retinal images would more accurately correlate with future ASCVD events compared to measured SBP. Methods: 95,665 macula-centered retinal images drawn from the 51,778 individuals in the UK Biobank who had not experienced an ASCVD event prior to retinal imaging were used. A deep-learning model was trained to predict an individual's SBP. The correlation of subsequent ASCVD events with the AI-predicted SBP and the mean of the measured SBP acquired at the time of retinal imaging was determined and compared. Results: The overall ASCVD event rate observed was 3.4%. The correlation between SBP and future ASCVD events was significantly higher if the AI-predicted SBP was used compared to the measured SBP: 0.067 v 0.049, Conclusions: With the variability and challenges of real-world SBP measurement, AI analysis of retinal images may provide a more reliable and accurate biomarker for predicting future ASCVD events than traditionally measured SBP.
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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.