Work

Prediction of Cardiovascular Parameters With Supervised Machine Learning From Singapore “I” Vessel Assessment and OCT-Angiography: A Pilot Study

Year: 2021

Type: article

Abstract: Purpose: Assessment 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. Methods: We 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. Results: The 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. Conclusions: Performance 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 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.

Source: Translational Vision Science & Technology

DOI: 10.1167/tvst.10.13.20