Artificial intelligence in cardiovascular prevention: new ways will open new doors

Abstract

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

“…This would impact the recommendation to patients for use of this technology. AI models are also enhancing prediction and diagnosis of complications of diabetes which can lead to adverse cardiovascular events [17]. Other AI models like machine learning algorithms have been used to predict factors contributing to hypertension and hyperlipidemia, which is particularly geared toward clinician use.…”

“…The use of machine learning to improve prediction performance of the so called "double-hyper disease" (hypertension and hyperlipidemia) has important practical and clinical significance. Machine learning has also demonstrated high sensitivity for predicting new-onset hypertension [17]. Another promising use of machine learning is photoplethysmography for blood pressure estimation [19], useful from both a patient-use and clinician standpoint, leading to more accurate continuous blood pressure monitoring.…”

“…The natural history of heart failure is characterized by periods of clinical stability, alternating with phases of exacerbation, eventually resulting in a progressive decline in a patient's functional capacity [17,52]. Artificial intelligence and machine learning has attempted to simplify several aspects of heart failure care from a clinician standpoint, including diagnosis, classification, estimation of severity, and prediction of rehospitalization [17].…”

Interventions to Address Cardiovascular Risk in Obese Patients: Many Hands Make Light Work

“…Artificial Intelligence (AI) is the technological acquisition of knowledge and skill by a nonhuman device that, after being programmed initially, performs adaptive output tasks based on data input learnings [ 184 ]. More precisely, AI has several areas of opportunity in CV prevention and the battle against chronic diseases such as obesity [ 19 , 32 , 103 ]. Firstly, AI may help doctors who specialize in obesity by offering interactive programming related to analyses of body composition imaging for more accurate valuation of anthropometrics, behavior coaching, personal nutritional intervention, and physical activity recommendations, predictive modeling for identifying patients at risk for obesity-related complications, and implementation of precision/personalized medicine [ 184 , 185 , 186 , 187 , 188 ].…”

“…Indeed, several obesity-related mechanisms drive structural, functional, humoral, and hemodynamic alterations believed to underpin the development of CVD ( Figure 1 ) [ 1 ]. Specifically, there is a causal association between adipose tissue and CVD: obesity is the causal pathway of several traditional CV risk factors such as atherogenic dyslipidemia, hypertension and diabetes; moreover, obesity-related OSA contributes to CVD risk with the promotion of hypoxia, cardiac dysrhythmias, insulin resistance, and hypertension [ 31 , 32 , 33 , 34 ]. Obesity often leads to a deterioration of adipose tissue plasticity, which is linked to fibrosis, inflammation, progenitor cell senescence, and catecholamine resistance [ 35 ].…”