| Title: | Blood Pressure Predicted From Artificial Intelligence Analysis of Retinal Images Correlates With Future Cardiovascular Events |
| Journal: | JACC Advances |
| Published: | 18 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39629061/ |
| DOI: | https://doi.org/10.1016/j.jacadv.2024.101410 |
Publication 14303
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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.</p>
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.</p>
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.</p>
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, P = 0.008. Variability in measured SBP in UK Biobank was present (mean absolute difference = 8.2 mm Hg), which impacted the 10-year ASCVD risk score in 6% of the participants.</p>
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.</p>
7 Authors
- David M. Squirrell
- Song Yang
- Li Xie
- Songyang Ang
- Mohammadi Moghadam
- Ehsan Vaghefi
- Michael V. McConnell
1 Application
| Application ID | Title |
|---|---|
| 86299 | Predicting Cardiovascular risk from retinal photographs and limited demographic data: An external validation of a New Zealand algorithm |
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