AIMC Topic: Catheter Ablation
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An advanced robotic system incorporating haptic feedback for precision cardiac ablation procedures.
Scientific reports
Feb 26, 2025
This study introduces an innovative master-slave cardiac ablation catheter robot system that employs magnetorheological fluids. The system incorporates magnetorheological fluid to enable collision detection through haptic feedback, thereby enhancing ...
Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.
Nature medicine
Feb 14, 2025
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...
An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.
JMIR human factors
Feb 13, 2025
BACKGROUND: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated poten...
Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.
JMIR cardio
Feb 11, 2025
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...
Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.
Journal of medical Internet research
Feb 10, 2025
BACKGROUND: Although catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potenti...
Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.
Circulation. Arrhythmia and electrophysiology
Feb 10, 2025
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...
[Investigation of impedance drop during lesions created by robot-assisted radiofrequency ablation].
Orvosi hetilap
Feb 9, 2025
12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.
Journal of electrocardiology
Dec 25, 2024
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 ...
Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.
Journal of cardiovascular electrophysiology
Dec 23, 2024
INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis co...
Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.
BMC cardiovascular disorders
Dec 19, 2024
BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of catheter ablation is crucial for patient selection and management. This research seeks to create a machi...
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