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Blood Pressure Predicted From Artificial Intelligence Analysis of Retinal Images Correlates With Future
David M Squirrell1, Song Yang1, Li Xie1
1Division of Artificial Intelligence, Toku Eyes, Auckland, New Zealand.
Insights
Artificial intelligence (AI) analyzing retinal images can predict systolic blood pressure (SBP) more accurately than traditional measurements. This AI-driven SBP prediction shows a stronger correlation with future atherosclerotic cardiovascular disease (ASCVD) events.
Area of Science:
- Ophthalmology
- Cardiology
- Artificial Intelligence
Background:
- High systolic blood pressure (SBP) is a major modifiable risk factor for premature cardiovascular death.
- Retinal vascular changes are linked to high SBP and correlate with atherosclerotic cardiovascular disease (ASCVD) events.
Purpose of the Study:
- To evaluate if AI-predicted SBP from retinal images correlates better with future ASCVD events than measured SBP.
- To compare the predictive accuracy of AI-derived SBP versus traditional SBP measurements for ASCVD risk.
Main Methods:
- Utilized 95,665 macula-centered retinal images from 51,778 UK Biobank participants without prior ASCVD events.
- Trained a deep-learning model to predict SBP from retinal images.
- Compared the correlation of subsequent ASCVD events with AI-predicted SBP and measured SBP.
Main Results:
- The correlation between SBP and future ASCVD events was significantly higher for AI-predicted SBP (0.067) compared to measured SBP (0.049), P = 0.008.
- Observed an overall ASCVD event rate of 3.4%.
- Identified variability in measured SBP (mean absolute difference = 8.2 mm Hg) impacting 10-year ASCVD risk scores in 6% of participants.
Conclusions:
- AI analysis of retinal images offers a potentially more reliable biomarker for predicting future ASCVD events.
- Challenges in real-world SBP measurement highlight the potential of AI-driven retinal analysis.
- AI-predicted SBP may surpass traditional measurements in accuracy for forecasting ASCVD risk.
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, 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.
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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