AIMC Topic: Pulmonary Veins

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A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.

Scientific data
Accurate segmentation of pulmonary structures from computed tomography (CT) is critical for lung disease management, yet progress is hampered by a lack of large-scale, public datasets with comprehensive multi-structure annotations. To address this, w...

AI-guided spatiotemporal dispersion mapping for individualized ablation in an all-comer cohort with atrial fibrillation.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
BACKGROUND: Artificial intelligence (AI)-guided spatiotemporal dispersion (stD) mapping has been shown to improve outcomes in patients with persistent atrial fibrillation (AF). However, the relationship between stD mapping and markers of atrial cardi...

Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.

Nature communications
Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a s...

Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.

Nature medicine
Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fibrillation (AF), the optimal ablation procedure for persistent and long-standing persistent AF remains elusive. Targeting spatio-temporal electrogram d...

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.

JMIR cardio
BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity and mortality. Despite advancements in ablation techniques, predicting recurrence of AF remains a challenge, necessitating reliable models to identif...

Application of artificial intelligence to analyze data from randomized controlled trials: An example from DECAAF II.

Heart rhythm
BACKGROUND: Causal machine learning (ML) provides an efficient way of identifying heterogeneous treatment effect groups from hundreds of possible combinations, especially for randomized trial data.

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...

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 ...

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...