About this Research Topic

Background

Cardiovascular disease (CVD) is considered the leading cause of fatalities and morbidity globally, which makes it a crucial area of study for medical investigations. Taking medical decisions is difficult for cardiac specialists because of the demands for better healthcare and the conversion of the most recent scientific discoveries and knowledge into a workable plan. Artificial intelligence (AI) methods including deep learning and machine learning have the capacity to fundamentally alter how cardiologists practice cardiac care intending to enhance and optimize outcomes for CVD. With the latest advancement in cardiac imaging modalities, a large amount of cardiac data can be collected. Recent improvements in computer power and specifications have made it possible for AI technologies, particularly deep learning, to analyse this huge medical data more effectively and affordably. By integrating, modeling, classifying, and interpreting various types of information (such as demographics, laboratory findings, electronic medical archives, biomedical signals (ECG, PPG, etc..), biomedical images (MRI, CT, etc), acoustic heart waves, and biofluid dynamics), AI technologies could be capable of addressing complications in cardiovascular medicine. This will require collaboration and involvement of a variety of various capabilities, primarily from biomedical engineers, cardiologists, and computer scientists.

The role of AI to revolutionize cardiology is demonstrated by the diagnosis of non-invasive coronary artery disease, obstructive coronary artery disease, severe pulmonary embolism (PE), and other cardiovascular conditions, in addition to forecasting of negative consequences and side effects following therapy. Besides, AI facilitated the identification of malignant arrhythmias through wearable devices. Additionally, AI can aid in the prediction of heart failure patients, forecasting of the incidence of readmission in cardiac failure individuals, forecasting of irregular fractional flow reserve among individuals experiencing coronary computed tomography angiogram, and the determination of left ventricular ejection fraction. The continuous development of AI, the Internet of Things (IoT), and precision medicine will have a major impact on how cardiovascular medicine is practiced in the coming years.

This Research topic aims to highlight cutting-edge developments in artificial intelligence, particularly deep/machine learning in cardiovascular medicine. We encourage both original research and review articles. Articles can include, but are not limited to, the following topics:
• Clinical decision-making in cardiovascular care using AI;
• Cardiovascular medicine using machine learning and deep learning;
• Data mining and data analysis for aiding clinical decisions in cardiology;
• AI for cardiovascular protection pharmaceuticals and drug advancement; AI for cardiovascular disease diagnostics;
• AI-based precision medicine for cardiology;
• Cardiology intelligent instruments, smart sensors, and devices
• Techniques and models for predicting the cardiovascular health of the population using AI;
• AI ethics in cardiovascular medicine.
• Computer-aided diagnostic (CAD) and prognostics (CAP) tools in cardiovascular medicine.
• Biomedical image /signal processing in cardiology.
• AI in the cardiovascular examination for the study of cardiovascular disease.
• AI for identifying patients who will experience acute heart failure
• AI in cardiovascular electrophysiology and cardiac arrhythmia
• AI-based mobile system for managing cardiovascular health
• AI analysis of molecular information for heart condition risk prediction
• AI to personalize cardiovascular visualization and healthcare
• AI to forecast cardiac problems in patients with chronic kidney disease
• Blockchain-based technology combined with AI for cardiology
• Federated learning in cardiology.

Keywords: AI, Artificial Intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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