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Heart Atria

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Discrimination between RA and LA Sinus Rhythms using machine learning approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Atrial fibrillation (AF) is a common cardiac disease that potentially leads to fatal conditions. Machine Learning (ML) classification methods are widely used to distinguish between sinus rhythm and AF for post-ablation rhythms in ECG. However, intrac...

DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network.

Journal of imaging informatics in medicine
In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radiofrequency ablation surgery. Accurate classification of pulmonary vein anatomy assists physicians in...

Neural network reconstruction of the left atrium using sparse catheter paths.

International journal of computer assisted radiology and surgery
PURPOSE: Catheter-based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ...

Left Atrial Wall Thickness Measured by a Machine Learning Method Predicts AF Recurrence After Pulmonary Vein Isolation.

Journal of cardiovascular electrophysiology
BACKGROUND: Left atrial (LA) remodeling plays a significant role in the progression of atrial fibrillation (AF). Although LA wall thickness (LAWT) has emerged as an indicator of structural remodeling, its impact on AF outcomes remains unclear. We aim...

12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.

Journal of electrocardiology
BACKGROUND AND PURPOSE: Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage areas (LVAs) which are associated with poor ablation outcomes and increased thromboembolic risk. This ...

Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.

PloS one
PURPOSE: Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as significant contributors to cardiogenic embolism in non-valvular atrial fibrillation (NVAF). This study aimed to construct and validate an interpretable pre...

Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature.

Medical image analysis
Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points...

The impact of training image quality with a novel protocol on artificial intelligence-based LGE-MRI image segmentation for potential atrial fibrillation management.

Computer methods and programs in biomedicine
BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting up to 2 % of the population. Catheter ablation is a promising treatment for AF, particularly for paroxysmal AF patients, but it often has high recurrence rates. Dev...

High-Resolution Maps of Left Atrial Displacements and Strains Estimated With 3D Cine MRI Using Online Learning Neural Networks.

IEEE transactions on medical imaging
The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterization of LA motion and deformation but is lackin...