Related references
Note: Only part of the references are listed.Machine Learning and the Future of Cardiovascular Care JACC State-of-the-Art Review
Giorgio Quer et al.
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2021)
Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality
Sheojung Shin et al.
ESC HEART FAILURE (2021)
Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device
John R. Giudicessi et al.
CIRCULATION (2021)
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke
Sushravya Raghunath et al.
CIRCULATION (2021)
Machine Learning in Arrhythmia and Electrophysiology
Natalia A. Trayanova et al.
CIRCULATION RESEARCH (2021)
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
Konstantinos C. Siontis et al.
NATURE REVIEWS CARDIOLOGY (2021)
Applications of artificial intelligence in cardiovascular imaging
Maxime Sermesant et al.
NATURE REVIEWS CARDIOLOGY (2021)
Transfer learning for ECG classification
Kuba Weimann et al.
SCIENTIFIC REPORTS (2021)
Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram
J. Martijn Bos et al.
JAMA CARDIOLOGY (2021)
Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
Xiaoxi Yao et al.
NATURE MEDICINE (2021)
Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform
John S. Chorba et al.
JOURNAL OF THE AMERICAN HEART ASSOCIATION (2021)
Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy
Qiang Zhang et al.
CIRCULATION (2021)
Interpretable heartbeat classification using local model-agnostic explanations on ECGs
Ines Neves et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2021)
AI-Assisted Echocardiographic Prescreening of Heart Failure With Preserved Ejection Fraction on the Basis of Intrabeat Dynamics
Yu-An Chiou et al.
JACC-CARDIOVASCULAR IMAGING (2021)
Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography
Ivar M. Salte et al.
JACC-CARDIOVASCULAR IMAGING (2021)
Machine Learning of ECG Waveforms to Improve Selection for Testing for Asymptomatic Left Ventricular Dysfunction
Elizabeth L. Potter et al.
JACC-CARDIOVASCULAR IMAGING (2021)
AI Filter Improves Positive Predictive Value of Atrial Fibrillation Detection by an Implantable Loop Recorder
Suneet Mittal et al.
JACC-CLINICAL ELECTROPHYSIOLOGY (2021)
Who Will Pay for AI?
Melissa M. Chen et al.
RADIOLOGY-ARTIFICIAL INTELLIGENCE (2021)
Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG
Giorgio Luongo et al.
CARDIOVASCULAR DIGITAL HEALTH JOURNAL (2021)
2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society
Niraj Varma et al.
JOURNAL OF ARRHYTHMIA (2021)
Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging
Ernest V. Garcia et al.
JOURNAL OF NUCLEAR CARDIOLOGY (2020)
A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images
Kenya Kusunose et al.
JACC-CARDIOVASCULAR IMAGING (2020)
Integrating blockchain technology with artificial intelligence for cardiovascular medicine
Chayakrit Krittanawong et al.
NATURE REVIEWS CARDIOLOGY (2020)
Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score
Marton Tokodi et al.
EUROPEAN HEART JOURNAL (2020)
Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
Wei-Yin Ko et al.
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2020)
Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms
Prashnna Kumar Gyawali et al.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2020)
Automatic diagnosis of the 12-lead ECG using a deep neural network
Antonio H. Ribeiro et al.
NATURE COMMUNICATIONS (2020)
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness
Sebastian Vollmer et al.
BMJ-BRITISH MEDICAL JOURNAL (2020)
Arti ficial Intelligence in Cardiology: Present and Future
Francisco Lopez-Jimenez et al.
MAYO CLINIC PROCEEDINGS (2020)
Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology
Albert K. Feeny et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2020)
Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation
Julie K. Shade et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2020)
Prospective Assessment of an Automated Intraprocedural 12-Lead ECG-Based System for Localization of Early Left Ventricular Activation
Shijie Zhou et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2020)
In silicoComparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation
Caroline H. Roney et al.
FRONTIERS IN PHYSIOLOGY (2020)
Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation A Population-Based Study
Georgios Christopoulos et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2020)
Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study
Hongling Zhu et al.
LANCET DIGITAL HEALTH (2020)
Applications of Machine Learning in Cardiac Electrophysiology
Rahul G. Muthalaly et al.
ARRHYTHMIA & ELECTROPHYSIOLOGY REVIEW (2020)
Deep learning for cardiovascular medicine: a practical primer
Chayakrit Krittanawong et al.
EUROPEAN HEART JOURNAL (2019)
Smartwatch Performance for the Detection and Quantification of Atrial Fibrillation
Jeremiah Wasserlauf et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2019)
Machine Learning Prediction of Response to Cardiac Resynchronization Therapy Improvement Versus Current Guidelines
Albert K. Feeny et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2019)
Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention
Chad J. Zack et al.
JACC-CARDIOVASCULAR INTERVENTIONS (2019)
Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs
Zachi Attia et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2019)
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
Geoffrey H. Tison et al.
CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES (2019)
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
Zachi Attia et al.
LANCET (2019)
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Marco V. Perez et al.
NEW ENGLAND JOURNAL OF MEDICINE (2019)
Predicting atrial fibrillation in primary care using machine learning
Nathan R. Hill et al.
PLOS ONE (2019)
Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy
Maja Cikes et al.
EUROPEAN JOURNAL OF HEART FAILURE (2019)
Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram
Zachi I. Attia et al.
NATURE MEDICINE (2019)
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Awni Y. Hannun et al.
NATURE MEDICINE (2019)
A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
Sara Bersche Golas et al.
BMC MEDICAL INFORMATICS AND DECISION MAKING (2018)
Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes Lessons From the COMPANION Trial
Matthew M. Kalscheur et al.
CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY (2018)
Smartphone ECG for evaluation of ST-segment elevation myocardial infarction (STEMI): Design of the ST LEUIS International Multicenter Study
Alejandro Barbagelata et al.
JOURNAL OF ELECTROCARDIOLOGY (2018)
Clinical Implications of Technological Advances in Screening for Atrial Fibrillation
Nikhil Singh et al.
PROGRESS IN CARDIOVASCULAR DISEASES (2018)
Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study
Julian Betancur et al.
JACC-CARDIOVASCULAR IMAGING (2018)
Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers
Aurore Lyon et al.
FRONTIERS IN PHYSIOLOGY (2018)
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
Geoffrey H. Tison et al.
JAMA CARDIOLOGY (2018)
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Ryan Poplin et al.
NATURE BIOMEDICAL ENGINEERING (2018)
Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
Ioannis A. Kakadiaris et al.
JOURNAL OF THE AMERICAN HEART ASSOCIATION (2018)
Cardiac imaging: working towards fully-automated machine analysis & interpretation
Piotr J. Slomka et al.
EXPERT REVIEW OF MEDICAL DEVICES (2017)
Machine Learning Improves Risk Stratification After Acute Coronary Syndrome
Paul D. Myers et al.
SCIENTIFIC REPORTS (2017)
Analysis of Machine Learning Techniques for Heart Failure Readmissions
Bobak J. Mortazavi et al.
CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES (2016)
Analysis of Machine Learning Techniques for Heart Failure Readmissions
Bobak J. Mortazavi et al.
CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES (2016)
Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG
Zachi I. Attia et al.
JOURNAL OF THE AMERICAN HEART ASSOCIATION (2016)
Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population
Reza Arsanjani et al.
JOURNAL OF NUCLEAR CARDIOLOGY (2015)
Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population
Reza Arsanjani et al.
JOURNAL OF NUCLEAR CARDIOLOGY (2013)