AIMC Topic: Cardiovascular Diseases

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Artificial Intelligence and Cardiology Practice in Nigeria: Are We Ready?

Nigerian journal of clinical practice
Cardiovascular diseases are the leading cause of death globally. As cardiovascular risk factors continuously rise to pandemic levels, there is intense pressure worldwide to improve cardiac care in preventive cardiology, cardio-diagnostics, therapeuti...

Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

Journal of diabetes investigation
BACKGROUND: The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link between the progression of DKD and an increased risk of cardiovascular disease (CVD), it is crucial to...

Technological Advances in the Diagnosis of Cardiovascular Disease: A Public Health Strategy.

International journal of environmental research and public health
This article reviews technological advances and global trends in the diagnosis, treatment, and monitoring of cardiovascular diseases. A bibliometric analysis was conducted using the SCOPUS database, following PRISMA-ScR guidelines, to identify releva...

Artificial intelligence in cardiovascular imaging and intervention.

Herz
Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular ...

Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts.

BMC public health
BACKGROUND: The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle i...

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.

Biomedical engineering online
BACKGROUND: Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted inc...

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

Journal of medical Internet research
In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiov...

Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility.

Internal medicine (Tokyo, Japan)
Artificial intelligence (AI) has shown transformative potential in various medical fields, including diagnostic imaging. Recent advances in AI-driven technologies have opened new avenues for improving echocardiographic practices. AI algorithms enhanc...

Unlocking Tomorrow's Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence.

The Canadian journal of cardiology
As an integral aspect of health care, digital technology has enabled modelling of complex relationships to detect, screen, diagnose, and predict patient outcomes. With massive data sets, artificial intelligence (AI) can have marked effects on 3 level...