Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review

R Khera, EK Oikonomou, GN Nadkarni… - Journal of the American …, 2024 - jacc.org
Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice
and research. The exponential rise in technology powered by AI is defining new frontiers in …

DNA methylation in cardiovascular disease and heart failure: novel prediction models?

A Desiderio, M Pastorino, M Campitelli, M Longo… - Clinical …, 2024 - Springer
Background Cardiovascular diseases (CVD) affect over half a billion people worldwide and
are the leading cause of global deaths. In particular, due to population aging and worldwide …

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease

L Pastika, A Sau, K Patlatzoglou, E Sieliwonczyk… - npj Digital …, 2024 - nature.com
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial
intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI …

Ecg-fm: An open electrocardiogram foundation model

K McKeen, L Oliva, S Masood, A Toma, B Rubin… - arXiv preprint arXiv …, 2024 - arxiv.org
The electrocardiogram (ECG) is a ubiquitous diagnostic test. Conventional task-specific
ECG analysis models require large numbers of expensive ECG annotations or associated …

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

M Salvi, MR Acharya, S Seoni, O Faust… - … : Data Mining and …, 2024 - Wiley Online Library
Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most
prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent …

Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications

MS Islam, SV Kalmady, A Hindle, R Sandhu… - Canadian Journal of …, 2024 - Elsevier
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the
potential to transform diagnosis and estimate the prognosis of not only cardiac but …

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

A Sau, L Pastika, E Sieliwonczyk… - The Lancet Digital …, 2024 - thelancet.com
Background Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to
predict risk of future disease and mortality but has not yet been adopted into clinical practice …

Present results and methods of vectorcardiographic diagnostics of ischemic heart disease

J Kijonka, P Vavra, M Penhaker, D Bibbo… - Computers in Biology …, 2023 - Elsevier
This article presents an overview of existing approaches to perform vectorcardiographic
(VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided …

[HTML][HTML] Sex-specific cardiovascular risk factors in the UK Biobank

SR St Pierre, B Kaczmarski, M Peirlinck… - Frontiers in Physiology, 2024 - frontiersin.org
The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of
women compared to that of men. For more than half a century, the Framingham Risk Score …

Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort

F Qafoud, K Kunji, M Elshrif, A Althani, A Salam… - Journal of Clinical …, 2024 - mdpi.com
Background: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool
used in clinical medicine to assess the electrical activity of the heart while the patient is …