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.

项目摘要 心房功能（AF）是最普遍的心律不齐，导致发病率和死亡率 1-2％的人口，并为全球医疗保健成本做出了重要贡献。对于AF可以的患者 不受药物治疗，推荐的治疗是基于导管的消融，以分离心律不齐的触发因素和 消除心律不齐的底物。节律控制中导管消融的成功率 AF患者为50-75％，持续性AF的患者较差。基线和基线的机制 开通后房屋重塑，包括心房扩张，功能障碍和纤维化，有助于AF 导管消融后的复发，尚不清楚，底层因素没有特征 训练。了解毒品不佳的AF患者的心房重塑并发现新的个性化 成功消融和预防AF复发的策略是对临床的追求 有能力。迫切需要开发新的消融方法 对心房基质后期的重塑，从而改善了治疗的效率并消除 重复程序。 该应用的总体目的是使用成像，艺术智能的新颖组合 （AI），电解映射和机械计算建模，以了解原因 药物难治性AF患者的AF复发，并为个性化消融开发新的范式 这消除了重复程序。利用我们在获得高质量心房im的进步方面的进步 年龄，我们在人工智能，特别深度学习方面的专业知识，以及我们有效地产生个性化的能力 推杆房间模型，我们建议表征基线心房重塑的形状，结构和功能 以及其进展后处理。使用获得的见解，我们将开发全面的ABLA- 通过基于机械的增强学习来确定AF消融目标的策略 在拟议的研究中获得的知识。该项目将在一项试验前瞻性患者研究中达到高潮 将测试新的消融策略。该项目的成功执行将为范式转移铺平 AF消融的临床程序，并寻求消除毒品不良AF患者的重复程序， 导致疗法的效率有了显着改善。重要的是，该项目的完成将是 诊断和治疗中成像，AI和计算建模的整合中的重大飞跃 心律障碍。