AIMC Topic: Cardiovascular Diseases

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Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.

Cell metabolism
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpreta...

hART: Deep learning-informed lifespan heart failure risk trajectories.

International journal of medical informatics
BACKGROUND: Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD).

Implications of Bias in Artificial Intelligence: Considerations for Cardiovascular Imaging.

Current atherosclerosis reports
PURPOSE OF REVIEW: Bias in artificial intelligence (AI) models can result in unintended consequences. In cardiovascular imaging, biased AI models used in clinical practice can negatively affect patient outcomes. Biased AI models result from decisions...

Improving deep-learning electrocardiogram classification with an effective coloring method.

Artificial intelligence in medicine
Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis a...

Prediction of certainty in artificial intelligence-enabled electrocardiography.

Journal of electrocardiology
BACKGROUND: The 12‑lead ECG provides an excellent substrate for artificial intelligence (AI) enabled prediction of various cardiovascular diseases. However, a measure of prediction certainty is lacking.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.

BMC medicine
BACKGROUND: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, descr...

The association between vitamin D deficiency and the clinical outcomes of hospitalized COVID-19 patients.

F1000Research
BACKGROUND: Vitamin D deficiency is an emerging public health problem that affects more than one billion people worldwide. Vitamin D has been shown to be effective in preventing and reducing the severity of viral respiratory diseases, including influ...

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

Scientific reports
It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE)...

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Network (Bristol, England)
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification...

AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

Abdominal radiology (New York)
PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition m...