Application of machine learning in understanding atherosclerosis: Emerging insights.

Eric Munger,John W Hickey,Jason M Kinser,Nehal N Mehta,Amit K Dey,Mohsin Saleet Jafri

doi:10.1063/5.0028986

Abstract

Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

Highlights

Cardiovascular disease (CVD) including heart attack and stroke is the leading cause of death worldwide and is usually preceded by accelerated atherosclerosis.[1]
There has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multimodality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation
The analysis showed that the top-20 clinically significant biomarkers were body mass index, visceral adiposity, total adiposity, apolipoprotein A1, high-density lipoprotein, erythrocyte sedimentation rate, subcutaneous adiposity, small low-density lipoprotein particle, cholesterol efflux capacity, absolute granulocyte count, total cholesterol, waist-to-hip ratio, apolipoprotein B, very-low-density lipoprotein particle, absolute scitation.org/journal/apb monocyte count, high-sensitivity C-reactive protein, large very-low-density lipoprotein particle, large medium high-density lipoprotein particle, large medium very-low-density lipoprotein particle, and white blood cells (Fig. 2—use of coronary computed tomography angiography in psoriasis).[6]

Summary

Introduction

Cardiovascular disease (CVD) including heart attack and stroke is the leading cause of death worldwide and is usually preceded by accelerated atherosclerosis.[1].

Results

Conclusion