scholarly journals Corrigendum: Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation

2021 ◽  
Vol 12 ◽  
Author(s):  
Laila Muizniece ◽  
Adrian Bertagnoli ◽  
Ahmed Qureshi ◽  
Aya Zeidan ◽  
Aditi Roy ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Reinforcement Learning ◽  
Image Guidance ◽  
Ablation Therapy

scholarly journals Reinforcement Learning to Improve Image-Guidance of Ablation Therapy for Atrial Fibrillation

2021 ◽  
Vol 12 ◽  
Author(s):  
Laila Muizniece ◽  
Adrian Bertagnoli ◽  
Ahmed Qureshi ◽  
Aya Zeidan ◽  
Aditi Roy ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Reinforcement Learning ◽  
Success Rate ◽  
Recurrence Rate ◽  
Image Guidance ◽  
Treatment Success ◽  
Patient Specific ◽  
Functional Information ◽  
Q Learning ◽  
Af Recurrence

Atrial fibrillation (AF) is the most common cardiac arrhythmia and currently affects more than 650,000 people in the United Kingdom alone. Catheter ablation (CA) is the only AF treatment with a long-term curative effect as it involves destroying arrhythmogenic tissue in the atria. However, its success rate is suboptimal, approximately 50% after a 2-year follow-up, and this high AF recurrence rate warrants significant improvements. Image-guidance of CA procedures have shown clinical promise, enabling the identification of key patient anatomical and pathological (such as fibrosis) features of atrial tissue, which require ablation. However, the latter approach still suffers from a lack of functional information and the need to interpret structures in the images by a clinician. Deep learning plays an increasingly important role in biomedicine, facilitating efficient diagnosis and treatment of clinical problems. This study applies deep reinforcement learning in combination with patient imaging (to provide structural information of the atria) and image-based modelling (to provide functional information) to design patient-specific CA strategies to guide clinicians and improve treatment success rates. To achieve this, patient-specific 2D left atrial (LA) models were derived from late-gadolinium enhancement (LGE) MRI scans of AF patients and were used to simulate patient-specific AF scenarios. Then a reinforcement Q-learning algorithm was created, where an ablating agent moved around the 2D LA, applying CA lesions to terminate AF and learning through feedback imposed by a reward policy. The agent achieved 84% success rate in terminating AF during training and 72% success rate in testing. Finally, AF recurrence rate was measured by attempting to re-initiate AF in the 2D atrial models after CA with 11% recurrence showing a great improvement on the existing therapies. Thus, reinforcement Q-learning algorithms can predict successful CA strategies from patient MRI data and help to improve the patient-specific guidance of CA therapy.

Role of the Ambulatory Assessed Apnea-Hypopnea Index for Predicting Recurring Atrial Fibrillation After Ablation Therapy

2021 ◽  
Author(s):  
Teresa Trenkwalder ◽  
Christian Grebmer ◽  
Madeleine Tydecks ◽  
Patrick Blažek ◽  
Marc Kottmaier ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Apnea Hypopnea Index ◽  
Ablation Therapy

Ablation Therapy for Atrial Fibrillation: Implications for the Anesthesiologist

2015 ◽  
Vol 29 (5) ◽  
pp. 1341-1356 ◽  
Author(s):  
Himani V. Bhatt ◽  
Georgios Syros ◽  
Michael Greco ◽  
Marc Miller ◽  
Gregory W. Fischer
Keyword(s):  
Atrial Fibrillation ◽  
Ablation Therapy

Pulmonary Venous Occlusion as a Complication of Ablation Therapy for Atrial Fibrillation

2018 ◽  
Vol 54 (6) ◽  
pp. 338-340
Author(s):  
Laura Fernández-Navarro ◽  
Elena Moya-Sánchez ◽  
Diego Segura-Rodríguez ◽  
Eduardo Ruiz-Carazo
Keyword(s):  
Atrial Fibrillation ◽  
Venous Occlusion ◽  
Ablation Therapy

scholarly journals Influence of Sleep-Disorder Breathing on Recurrence in Patients with Catheter Ablation Therapy for Atrial Fibrillation

2011 ◽  
Vol 27 (Supplement) ◽  
pp. PJ1_023
Author(s):  
Kensuke Fujiwara ◽  
Koji Abe ◽  
Chizuru Sato ◽  
Satoru Komura ◽  
Atsushi Iwasa
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Sleep Disorder ◽  
Sleep Disorder Breathing ◽  
Ablation Therapy

Early rhythm‐control ablation therapy to prevent atrial fibrillation recurrences: Insights from the CHARISMA Registry

2021 ◽  
Author(s):  
Francesco Solimene ◽  
Mario Giannotti Santoro ◽  
Giuseppe Stabile ◽  
Maurizio Malacrida ◽  
Antonio De Simone ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Rhythm Control ◽  
Ablation Therapy

Atrial fibrillation surgery with a focus on patients with reduced left ventricular function and heart failure

EP Europace ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 517-521
Author(s):  
Johannes Petersen ◽  
Hermann Reichenspurner ◽  
Simon Pecha
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Surgical Treatment ◽  
Left Ventricular Function ◽  
Treatment Options ◽  
Left Ventricular ◽  
Review Article ◽  
Surgical Ablation ◽  
Ablation Therapy ◽  
Patients With Heart Failure

Abstract This review article aims to give an overview on the different surgical treatment options for atrial fibrillation It includes concomitant- as well as stand-alone surgical ablation therapy and outlines the main issues in patients with heart failure and reduced LVEF.

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