Machine learning in precision diabetes care and cardiovascular risk prediction
EK Oikonomou, R Khera - Cardiovascular Diabetology, 2023 - Springer
Artificial intelligence and machine learning are driving a paradigm shift in medicine,
promising data-driven, personalized solutions for managing diabetes and the excess …
promising data-driven, personalized solutions for managing diabetes and the excess …
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from
electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but …
electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but …
Artificial Intelligence for Cardiovascular Care—Part 1: Advances: JACC Review Topic of the Week
P Elias, SS Jain, T Poterucha, M Randazzo… - Journal of the American …, 2024 - jacc.org
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential
enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on …
enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on …
Automation bias and assistive AI: risk of harm from AI-driven clinical decision support
At the point of care, artificial intelligence (AI) algorithms have been developed to augment
diagnostic decisions and suggest appropriate care pathways, 1 by leveraging complex …
diagnostic decisions and suggest appropriate care pathways, 1 by leveraging complex …
[HTML][HTML] Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning
Background: Hypertrophic cardiomyopathy (HCM) affects 1 in every 200 individuals and is
the leading cause of sudden cardiac death in young adults. HCM can be identified using an …
the leading cause of sudden cardiac death in young adults. HCM can be identified using an …
Pediatric ECG-based deep learning to predict left ventricular dysfunction and remodeling
BACKGROUND: Artificial intelligence–enhanced ECG analysis shows promise to detect
ventricular dysfunction and remodeling in adult populations. However, its application to …
ventricular dysfunction and remodeling in adult populations. However, its application to …
Biometric contrastive learning for data-efficient deep learning from electrocardiographic images
Objective Artificial intelligence (AI) detects heart disease from images of electrocardiograms
(ECGs). However, traditional supervised learning is limited by the need for large amounts of …
(ECGs). However, traditional supervised learning is limited by the need for large amounts of …
A Multicenter Evaluation of the Impact of Procedural and Pharmacological Interventions on Deep Learning-based Electrocardiographic Markers of Hypertrophic …
Artificial intelligence–enhanced electrocardiography (AI–ECG) interpretation can identify
hypertrophic cardiomyopathy (HCM) on 12–lead ECGs and is a novel way to monitor …
hypertrophic cardiomyopathy (HCM) on 12–lead ECGs and is a novel way to monitor …
[HTML][HTML] Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study
Background: Current risk stratification strategies for heart failure (HF) risk require either
specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we …
specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we …
[HTML][HTML] Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images
Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction
(CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability …
(CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability …