Abstract
PurposeAssessment of cardiovascular risk is the keystone of prevention in cardiovascular disease. The objective of this pilot study was to estimate the cardiovascular risk score (American Hospital Association [AHA] risk score, Syntax risk, and SCORE risk score) with machine learning (ML) model based on retinal vascular quantitative parameters.MethodsWe proposed supervised ML algorithm to predict cardiovascular parameters in patients with cardiovascular diseases treated in Dijon University Hospital using quantitative retinal vascular characteristics measured with fundus photography and optical coherence tomography – angiography (OCT-A) scans (alone and combined). To describe retinal microvascular network, we used the Singapore “I” Vessel Assessment (SIVA), which extracts vessel parameters from fundus photography and quantitative OCT-A retinal metrics of superficial retinal capillary plexus.ResultsThe retinal and cardiovascular data of 144 patients were included. This paper presented a high prediction rate of the cardiovascular risk score. By means of the Naïve Bayes algorithm and SIVA + OCT-A data, the AHA risk score was predicted with 81.25% accuracy, the SCORE risk with 75.64% accuracy, and the Syntax score with 96.53% of accuracy.ConclusionsPerformance of these algorithms demonstrated in this preliminary study that ML algorithms applied to quantitative retinal vascular parameters with SIVA software and OCT-A were able to predict cardiovascular scores with a robust rate. Quantitative retinal vascular biomarkers with the ML strategy might provide valuable data to implement predictive model for cardiovascular parameters.Translational RelevanceSmall data set of quantitative retinal vascular parameters with fundus and with OCT-A can be used with ML learning to predict cardiovascular parameters.
Highlights
We proposed supervised machine learning (ML) algorithm to predict cardiovascular parameters in patients with cardiovascular diseases treated in Dijon University Hospital using quantitative retinal vascular characteristics measured with fundus photography and optical coherence tomography – angiography (OCT-A) scans
Performance of these algorithms demonstrated in this preliminary study that ML algorithms applied to quantitative retinal vascular parameters with Singapore “I” Vessel Assessment (SIVA) software and optical coherence tomography angiography (OCT-A) were able to predict cardiovascular scores with a robust rate
Translational Relevance: Small data set of quantitative retinal vascular parameters with fundus and with OCT-A can be used with ML learning to predict cardiovascular parameters
Summary
We hypothesized that classic ML algorithms could predict cardiovascular risk scores and systemic parameters from quantitative retinal vascular data obtained with SIVA software and OCT-A. Better cardiovascular risk stratification is of growing interest given that cardiovascular disease (CVD) remains one of the leading causes of death worldwide.[14] The purpose of this pilot study was to develop a prediction model with a supervised ML approach to cardiovascular parameters using retinal vascular characteristics measured with SIVA software and OCT-A (alone and combined). Our objective was to estimate the cardiovascular risk score (American Hospital Association [AHA] risk score, Syntax score, and SCORE risk) with this ML model
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