Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization

• Published in: The International Journal of Cardiovascular Imaging Vol. 37; no. 11; pp. 3145 - 3156
• Main Authors: Johri, Amer M., Mantella, Laura E., Jamthikar, Ankush D., Saba, Luca, Laird, John R., Suri, Jasjit S.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2021; Springer Nature B.V
• Subjects: Algorithms; Angiography; Artificial intelligence; Cardiac Imaging; Cardiology; Cardiovascular disease; Cardiovascular diseases; Coronary artery; Coronary artery disease; Health hazards; Health risks; Heart diseases; Imaging; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Multivariate analysis; Original Paper; Phenotypes; Predictions; Radiology; Statistical methods; Statistical models; Statistical prediction; Statistics; Survival; Survival analysis; Ultrasonic imaging; Ultrasound; Vascularization; Risk prediction; And cardiovascular event prediction; Intraplaque neovascularization; Coronary artery disease; Machine learning; Focused carotid ultrasound
• ISSN: 1569-5794; 1573-0743; 1875-8312
• DOI: 10.1007/s10554-021-02294-0

The aim of this study was to compare machine learning (ML) methods with conventional statistical methods to investigate the predictive ability of carotid plaque characteristics for assessing the risk of coronary artery disease (CAD) and cardiovascular (CV) events. Focused carotid B-mode ultrasound, contrast-enhanced ultrasound, and coronary angiography were performed on 459 participants. These participants were followed for 30 days. Plaque characteristics such as carotid intima-media thickness (cIMT), maximum plaque height (MPH), total plaque area (TPA), and intraplaque neovascularization (IPN) were measured at baseline. Two ML-based algorithms—random forest (RF) and random survival forest (RSF) were used for CAD and CV event prediction. The performance of these algorithms was compared against (i) univariate and multivariate analysis for CAD prediction using the area-under-the-curve (AUC) and (ii) Cox proportional hazard model for CV event prediction using the concordance index (c-index). There was a significant association between CAD and carotid plaque characteristics [cIMT (odds ratio (OR) = 1.49, p = 0.03), MPH (OR = 2.44, p < 0.0001), TPA (OR = 1.61, p < 0.0001), and IPN (OR = 2.78, p < 0.0001)]. IPN alone reported significant CV event prediction (hazard ratio = 1.24, p < 0.0001). CAD prediction using the RF algorithm reported an improvement in AUC by ~  3% over the univariate analysis with IPN alone (0.97 vs. 0.94, p < 0.0001). Cardiovascular event prediction using RSF demonstrated an improvement in the c-index by ~  17.8% over the Cox-based model (0.86 vs . 0.73). Carotid imaging phenotypes and IPN were associated with CAD and CV events. The ML-based system is superior to the conventional statistically-derived approaches for CAD prediction and survival analysis.