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Cardiovascular Diseases

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Development and validation of cardiometabolic risk predictive models based on LDL oxidation and candidate geromarkers from the MARK-AGE data.

Mechanisms of ageing and development
The predictive value of the susceptibility to oxidation of LDL particles (LDLox) in cardiometabolic risk assessment is incompletely understood. The main objective of the current study was to assess its relationship with other relevant biomarkers and ...

Application of AI-empowered scenario-based simulation teaching mode in cardiovascular disease education.

BMC medical education
BACKGROUND: Cardiovascular diseases present a significant challenge in clinical practice due to their sudden onset and rapid progression. The management of these conditions necessitates cardiologists to possess strong clinical reasoning and individua...

Image-based ECG analyzing deep-learning algorithm to predict biological age and mortality risks: interethnic validation.

Journal of cardiovascular medicine (Hagerstown, Md.)
BACKGROUND: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI...

Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.

European journal of medical research
BACKGROUND: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in ...

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

International journal of molecular sciences
The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedentary lifestyle behaviors. These are all linked to the altered function of the mesocorticolimbic (MCL)...

Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.

BMC medical informatics and decision making
BACKGROUND: Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist ...

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular diseases (CVDs). It is important to consider both temporal and spatial features in ECG analysis to improve automated CVDs diagnosis. Significant progre...

A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis ...

Detection and classification of electrocardiography using hybrid deep learning models.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
OBJECTIVE: Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to ex...