Artificial intelligence analysis of atrial remodeling evolution in patients with atrial fibrillation: Towards optimal ablation strategies

心房颤动患者心房重塑演变的人工智能分析：寻求最佳消融策略

• 批准号: 10559270
• 负责人: Eugene Kholmovski
• 金额: $ 79.86万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-20 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559270
• 关键词: Ablation; American; Arrhythmia; Artificial Intelligence; Atrial Fibrillation; Cardiac ablation; Catheters; Clinical; Clinical Data; Computer Models; Development; Diagnosis; Disease; Electrophysiology (science); Environment; Evolution; Fibrosis; Health Care Costs; Heart Atrium; Image; Knowledge acquisition; Lead; Learning; Link; Longitudinal Studies; Machine Learning; Maps; Medical; Modeling; Morbidity - disease rate; Morphology; Patients; Pharmaceutical Preparations; Population; Prevention; Procedures; Psychological reinforcement; Public Health; Pulmonary veins; Recommendation; Recurrence; Refractory; Research; Role; Shapes; Structure; Testing; Time; Treatment Efficacy; cardiac magnetic resonance imaging; clinically significant; contrast enhanced; deep learning; deep reinforcement learning; economic impact; functional disability; global health; health goals; heart rhythm; improved; indexing; individual patient; insight; mortality; novel; novel strategies; patient stratification; personalized strategies; prospective; risk minimization; stem; success; treatment optimization

PROJECT SUMMARY Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, leading to morbidity and mortality in 1-2% of the population and contributing significantly to global health care costs. For patients in whom AF can- not be treated by drugs, the recommended therapy is catheter-based ablation to isolate arrhythmia triggers and eliminate the substrate for arrhythmia perpetuation. The success rate of catheter ablation in rhythm controlled AF patients is 50-75%, and is worse in patients with persistent AF. The mechanisms by which baseline and post-ablation atrial remodeling, including atrial distension, functional impairment, and fibrosis, contribute to AF recurrence following catheter ablation, are not well understood and the underling factors have not been charac- terized. Understanding atrial remodeling in drug-refractory AF patients and discovering new personalized strategies for successful AF ablation and prevention of AF recurrence is a quest of paramount clinical significance. There is an urgent need to develop new approaches to ablation that account mechanistically for the remodeling of the atrial substrate post-procedure, and thereby improve the efficacy of the therapy and eliminate repeat procedures. The overall objective of this application is to use novel combination of imaging, artificial intelligence (AI), electroanatomical mapping, and mechanistic computational modeling to understand the causes for AF recurrence in drug-refractory AF patients and to develop a new paradigm for personalized ablation that eliminates repeat procedures. Leveraging our advancements in the acquisition of high-quality atrial im- ages, our expertise in AI and particularly deep learning, and our ability to efficiently generate personalized com- putational atrial models, we propose to characterize baseline atrial remodeling in shape, structure and function as well as its progression post-procedure. Using the obtained insights, we will develop a comprehensive abla- tion strategy where AF ablation targets will be determined by reinforcement learning based on the mechanistic knowledge acquired in the proposed studies. The project will culminate in a pilot prospective patient study that will test the new ablation strategy. Successful execution of the project will pave the way for a paradigm shift in the clinical procedure of AF ablation and in the quest to eliminate repeat procedures in drug-refractory AF patients, resulting in a dramatic improvement in the efficacy of the therapy. Importantly, completion of this project will be major leap forward in the integration of imaging, AI, and computational modeling in the diagnosis and treatment of heart rhythm disorders.

项目总结