AIMC Topic: Cardiovascular Diseases

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The beating heart: artificial intelligence for cardiovascular application in the clinic.

Magma (New York, N.Y.)
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantl...

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.

The Science of the total environment
Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O forecasting to mitigate these risks, focusing on ...

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges.

The Canadian journal of cardiology
This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidl...

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults.

Neurology
BACKGROUND AND OBJECTIVES: Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential ...

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.

Circulation. Cardiovascular imaging
Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent he...

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In h...

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Scientific reports
Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scar...

Digital twins: reimagining the future of cardiovascular risk prediction and personalised care.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the potential to redefine cardiovascular risk prevention. Digital twinning combines vast amounts of data fr...

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.

American journal of physiology. Heart and circulatory physiology
The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thu...

ECG autoencoder based on low-rank attention.

Scientific reports
The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gai...