Artificial intelligence enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke

Abstract

AimsWe examine whether inclusion of Artificial Intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.MethodsAI-enabled retinal vessel image analysis processed images from 88,052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7,411 EPIC-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C- and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI).ResultsUKB prognostic models were developed on 65,144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5,862 EPIC-Norfolk participants (67.6, 9.1 years respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C- and R2 statistics between 0.75-0.77 and 0.33-0.44 respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS.ConclusionRV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk. (250 words)HIGHLIGHTSWhat is already known on this topicPopulation screening for MI and stroke using risk prediction tools exist but have limited uptake; risk scores for circulator mortality do not exist.What this study addsRisk models developed in UK Biobank (validated in EPIC-Norfolk) using Artificial Intelligence (AI)-enabled retinal vasculometry (RV), age, history of cardiovascular disease, use of hypertensive medication and smoking yielded high predictive test performance for circulatory mortality.Risk scores for MI and stroke performed similarly to established risk scores.How this study might affect research, practice or policyAI-enabled RV extraction offers a non-invasive prognostic biomarker of vascular health that does not require blood sampling or blood pressure measurement, and potentially has greater community reach to identify individuals at medium-high risk requiring further clinical assessment.SYNOPSIS/PRECISRisk models developed in UK Biobank (validated in EPIC-Norfolk) using Artificial Intelligence enabled retinal vasculometry indices, age, history of cardiovascular disease, use of hypertensive medication and smoking yielded high predictive test performance for circulatory mortality. Risk scores for MI and stroke performed similarly to established risk scores.

The eyes as a window to the heart: looking beyond the horizon

“…It also seems unlikely that retinal assessment alone could be used as a risk predictor, but it could indeed form part of a comprehensive CVD risk assessment incorporating other clinical risk factors. Even using AI, in this study by Rudnicka et al 21 around 20% of the images were not of sufficient quality to be assessed using QUARTZ. Looking to the future, there have been numerous studies reporting consistent associations between retinal vascular parameters and CVD prognosis.…”

“…Software tools have been developed to semi-automate the analysis of the retinal vasculature, [14][15][16][17] and morphometric measures such as vessel tortuosity, diameter and fractal dimension have again been associated with increased CVD risk in multiple studies. [18][19][20] Taking this a step further, in this issue, the paper by Rudnicka et al 21 describes the use of a fully automated artificial intelligence (AI)-enabled retinal assessment tool for prediction of CVD risk in two large population cohorts. The software tool, QUARTZ, computes estimates of vessel width, area and tortuosity efficiently.…”

Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review

“…Galtran et al [99] recently showed, through a detailed evaluation, that the performance of a simple downgraded UNet was equal to that of more complex models (several orders of magnitude in terms of model parameters) for vessel segmentation on fundus and OCT-A images. They also highlighted that these systems still exhibited a decreased performance across databases, leaving room for further improvements before application in daily clinical routine.…”