A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Abstract

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

“…Multilabel classification is not new [ 21 , 124 , 128 , 129 ]. For multilabel classification, the models are trained with multiple classes, for example, if there are two or more than two classes, then the gold standard must consist of two or more than two classes [ 124 , 129 ].…”

“…Multilabel classification is not new [ 21 , 124 , 128 , 129 ]. For multilabel classification, the models are trained with multiple classes, for example, if there are two or more than two classes, then the gold standard must consist of two or more than two classes [ 124 , 129 ]. Note that in our study, the only two classes used were COVID-19 and controls; however, different kinds of lesions can be classified using a multiclass-based classification framework (for example, GGO vs. consolidations vs. crazy paving), which was out of the scope of the current work, but this can be part of the future study.…”

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

“…These biases must be discussed in detail and considered while designing the clinical support systems. Recently, methods have been developed to compute biases, which can be extended for UNet-based systems [ 95 , 96 , 97 ].…”

Smartphone-enabled retinal arteriovenous imaging and correlation with coronary SYNTAX score

“…6 Machine learning models and artificial intelligence tools that combine conventional risk factors with imaging-based strategies may be more useful. 11 , 12 , 13 but needs ethnicity-specific risk factor data in view of varied proportionate attributable fractions in different populations. 26 Validation of such technologies also needs prospective evaluation and randomised clinical trials before they are widely implemented.…”

“… 8 , 10 Artificial intelligence and machine learning tools are now available to classify retinal vascular changes. 11 , 12 , 13 , 14 Smartphone technology is rapidly evolving and has gained widespread acceptance for performing point-of-care evaluation of retinal diseases such as retinopathy of prematurity and diabetes. 15 , 16 , 17 There have been sporadic case reports of the use of hand-held smartphone devices in conjunction with an ophthalmoscope for fundus AV ratio assessment, 18 , 19 but it has not been systematically evaluated for CAD risk assessment.…”