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Atrial Fibrillation

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Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique.

International journal of cardiology
BACKGROUND: Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automa...

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

Sensors (Basel, Switzerland)
The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a...

Automatic diagnosis of the 12-lead ECG using a deep neural network.

Nature communications
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has re...

Video-based AI for beat-to-beat assessment of cardiac function.

Nature
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac fun...

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

IEEE journal of biomedical and health informatics
This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibrillation (AF) with special emphasis on the combination of deep learning based algorithm and modified ...

Segmentation and visualization of left atrium through a unified deep learning framework.

International journal of computer assisted radiology and surgery
PURPOSE: Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause re...

Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, having been recognized as a true cardiovascular epidemic. In this paper, a new methodology for Computer Aided Diagnosis of AF based on a sp...

Machine learning detection of Atrial Fibrillation using wearable technology.

PloS one
BACKGROUND: Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stro...

A Catheter-Deployable Soft Robotic Inflatable Basket for Enhanced Conformability to the Left Atrium of the Heart.

Advanced healthcare materials
This paper presents the design, fabrication, and test results for a novel basket catheter that utilizes soft robotic technology, which can conform to complex patient anatomy. Two designs of basket-shaped balloons in three sizes are fabricated based o...

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

Journal of medical economics
As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effec...