AIMC Topic: Cardiovascular Diseases

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Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Nature communications
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and v...

Artificial intelligence in cardiovascular magnetic resonance imaging.

Radiologia
Artificial intelligence is rapidly evolving and its possibilities are endless. Its primary applications in cardiac magnetic resonance imaging have focused on: image acquisition (in terms of acceleration and quality improvement); segmentation (in term...

Life's Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Cardiovascular disease (CVD) and NAFLD share multiple common risk factors. Life's Crucial 9 (LC9), a novel indicator for comprehensive assessment of card...

Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.

Journal of the American Heart Association
Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large-scale, and dynamic data collection. Integrating EHR data with advanced analytical methods, including artificial intelligence (A...

Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management.

ACS applied materials & interfaces
Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Prolonged physical activity leads to notable cardiovascular adaptations, known as the athlete's heart,...

Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.

Computers in biology and medicine
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.

The Role of AI in Cardiovascular Event Monitoring and Early Detection: Scoping Literature Review.

JMIR medical informatics
BACKGROUND: Artificial intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including health care. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to b...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

European journal of epidemiology
Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in recent years. However, the interpretation of estimated individualized effects requires caution because insights from these data-driven approaches might b...

The Role of Artificial Intelligence in Cardiology.

Kardiologiia
Artificial intelligence (AI) has an enormous potential for improving the quality of medical care, diagnostic methods, and treatments. AI allows taking scientific research to a fundamentally new level. The article addresses the most important areas of...