Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Introduction Healthcare systems are complex and challenging for all stakeholders, but
artificial intelligence (AI) has transformed various fields, including healthcare, with the …
artificial intelligence (AI) has transformed various fields, including healthcare, with the …
Application of artificial intelligence to the electrocardiogram
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them
super-human diagnostic abilities. Trained without hard-coded rules by finding often …
super-human diagnostic abilities. Trained without hard-coded rules by finding often …
ECG-based deep learning and clinical risk factors to predict atrial fibrillation
Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate
efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether …
efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether …
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 …
rECHOmmend: an ECG-based machine learning approach for identifying patients at increased risk of undiagnosed structural heart disease detectable by …
Background: Timely diagnosis of structural heart disease improves patient outcomes, yet
many remain underdiagnosed. While population screening with echocardiography is …
many remain underdiagnosed. While population screening with echocardiography is …
Current and future use of artificial intelligence in electrocardiography
M Martínez-Sellés, M Marina-Breysse - Journal of Cardiovascular …, 2023 - mdpi.com
Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in
diagnosis, stratification, and management. AI algorithms can help clinicians in the following …
diagnosis, stratification, and management. AI algorithms can help clinicians in the following …
Automated multilabel diagnosis on electrocardiographic images and signals
The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms
(ECGs) can improve care in remote settings but is limited by the reliance on infrequently …
(ECGs) can improve care in remote settings but is limited by the reliance on infrequently …
A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased
morbidity and mortality. Its early detection is challenging because of the low detection yield …
morbidity and mortality. Its early detection is challenging because of the low detection yield …
[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
M Gadaleta, P Harrington, E Barnhill… - NPJ Digital …, 2023 - nature.com
Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and
other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even …
other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even …