AIMC Topic: Atrial Fibrillation

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Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography.

American journal of physiology. Heart and circulatory physiology Jun 25, 2021
Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for ...
Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Cardiomyopathy, Hypertrophic Automation Cardiomyopathy, Dilated Humans Female Aged Male Ventricular Function, Left Stroke Volume Echocardiography Middle Aged Deep Learning Atrial Fibrillation
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A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.

Scientific reports Jun 17, 2021
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 of conventional methods. We aimed to develop a deep learning-bas...
Humans Neural Networks, Computer Artificial Intelligence Algorithms Female Electrocardiography Atrial Fibrillation Probability Models, Cardiovascular Aged Sinoatrial Node Male Middle Aged Deep Learning ROC Curve
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Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.

American heart journal May 24, 2021
BACKGROUND: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.
Atrial Fibrillation Monitoring, Ambulatory Nervous System Diseases Outcome and Process Assessment, Health Care Undiagnosed Diseases Humans Artificial Intelligence Algorithms Female Adult Male Diagnosis, Computer-Assisted Remote Sensing Technology Patient Selection
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Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

JAMA network open May 3, 2021
IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic treatment (oral antiplatelets or anticoagulants) is limited by existing risk prediction models. Machine learning algorithms may result in superior pre...
Humans Machine Learning Retrospective Studies Clinical Decision-Making Female Male Middle Aged Risk Assessment Adolescent Cohort Studies Aged Aged, 80 and over Young Adult Adult Cross-Sectional Studies United States Venous Thromboembolism Predictive Value of Tests Antifibrinolytic Agents Fibrinolytic Agents Gastrointestinal Hemorrhage Anticoagulants Myocardial Ischemia Atrial Fibrillation Thienopyridines
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A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

BMC medical imaging Mar 9, 2021
OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.
Atrial Appendage Aged Atrial Fibrillation Male Heart Atria Humans Middle Aged Catheter Ablation Machine Learning ROC Curve Recurrence Prognosis Pulmonary Veins Retrospective Studies Female
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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.

Circulation Feb 16, 2021
BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could...
Electrocardiography Stroke Atrial Fibrillation Survival Analysis Humans Neural Networks, Computer Female Male Deep Learning
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Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.

Circulation. Arrhythmia and electrophysiology Feb 12, 2021
[Figure: see text].
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Risk Factors Catheter Ablation Recurrence Atrial Function, Left Predictive Value of Tests Fractals Radiographic Image Interpretation, Computer-Assisted Pulmonary Veins Atrial Fibrillation Heart Atria Heart Rate Male Middle Aged Risk Assessment Treatment Outcome Action Potentials
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An ECG Signal Classification Method Based on Dilated Causal Convolution.

Computational and mathematical methods in medicine Feb 2, 2021
The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an a...
Atrial Fibrillation Humans Neural Networks, Computer Algorithms Diagnosis, Computer-Assisted Databases, Factual Deep Learning Computational Biology Electrocardiography Signal Processing, Computer-Assisted
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Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

American journal of physiology. Heart and circulatory physiology Jan 29, 2021
Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demand...
Atrial Fibrillation Heart Rate Pattern Recognition, Automated Therapy, Computer-Assisted Heart Function Tests Clinical Decision-Making Humans Neural Networks, Computer Diagnosis, Computer-Assisted Artificial Intelligence Action Potentials Machine Learning Predictive Value of Tests Heart Conduction System
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Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

BMC cardiovascular disorders Jan 28, 2021
BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learnin...
Humans Machine Learning Genetic Association Studies Female Proteomics Male Signal Transduction Middle Aged Databases, Genetic Bayes Theorem Gene Expression Profiling Transcriptome Atrial Fibrillation Case-Control Studies Protein Interaction Maps Heart Atria Gene Regulatory Networks Heart Valve Diseases Oligonucleotide Array Sequence Analysis
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