AIMC Topic: Cardiovascular Diseases

Clear Filters Showing 211 to 220 of 736 articles

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...

Mitigating the risk of artificial intelligence bias in cardiovascular care.

The Lancet. Digital health
Digital health technologies can generate data that can be used to train artificial intelligence (AI) algorithms, which have been particularly transformative in cardiovascular health-care delivery. However, digital and health-care data repositories th...

The potential for large language models to transform cardiovascular medicine.

The Lancet. Digital health
Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early di...

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

World journal of pediatrics : WJP
BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics an...

Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophth...