AI Medical Compendium Journal:
Circulation. Arrhythmia and electrophysiology

Showing 21 to 25 of 25 articles

Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.

Circulation. Arrhythmia and electrophysiology Mar 18, 2020
BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-...
Humans Machine Learning Prognosis Predictive Value of Tests Registries Arrhythmias, Cardiac Contrast Media Magnetic Resonance Imaging Action Potentials Fourier Analysis Gadolinium DTPA Death, Sudden, Cardiac Heart Rate Myocardial Ischemia Cardiomyopathies Stroke Volume Middle Aged Risk Assessment Diagnosis, Computer-Assisted Imaging, Three-Dimensional United States Aged Male Ventricular Function, Left Retrospective Studies Female Risk Factors
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Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

Circulation. Arrhythmia and electrophysiology Feb 16, 2020
BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1)...
Deep Learning Electrocardiography Follow-Up Studies Male Ventricular Function, Left Middle Aged Heart Ventricles Ethnicity Racial Groups Humans Artificial Intelligence Retrospective Studies Female
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Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs.

Circulation. Arrhythmia and electrophysiology Aug 27, 2019
BACKGROUND: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained t...
Adolescent Electrocardiography Male Middle Aged Deep Learning Aged Young Adult Adult Humans Neural Networks, Computer Algorithms Artificial Intelligence Retrospective Studies Female
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Time Factors Recovery of Function Predictive Value of Tests Patient Selection Heart Failure Cardiac Resynchronization Therapy Middle Aged Risk Assessment Disease Progression Ventricular Function, Left Stroke Volume Humans Echocardiography Risk Factors Machine Learning Retrospective Studies Aged Decision Support Techniques Clinical Decision-Making Progression-Free Survival Male Female Heart Transplantation Practice Guidelines as Topic Ohio Heart-Assist Devices Baltimore
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Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.

Circulation. Arrhythmia and electrophysiology Jan 1, 2018
BACKGROUND: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study so...
Heart Failure Cardiac Resynchronization Therapy Heart Conduction System Ventricular Function, Left Stroke Volume Female Middle Aged Aged Male Deep Learning Humans Decision Making Machine Learning Predictive Value of Tests Algorithms
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