Application of Deep Learning to Predict Optimal Ablation Therapy for Atrial Fibrillation from Magnetic Resonance Imaging Data

应用深度学习根据磁共振成像数据预测心房颤动的最佳消融治疗

• 批准号: 2444971
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444971
• 关键词: Application; Deep; Learning; Predict; Optimal

The prevalence of atrial fibrillation (AF) is increasing in epidemic proportions: worldwide over 33 million individuals have AF (Chugh et al., 2014). The disease is associated with increased levels of morbidity and mortality, high risks of developing heart failure and stroke, and hence very high rates of patient hospitalizations. The overall economic burden of AF amounts to over 1% of total healthcare costs in the UK. Rhythm control strategies for maintaining sinus rhythm, such as antiarrhythmic drugs, can lead to significant improvements of cardiac output and quality of life. However, treatments of AF are complicated by its mechanisms for self-sustenance, such as the presence of AF-induced electrical and structural remodelling that generates more treatment-resistant arrhythmia (Nattel and Harada, 2014). Radiofrequency catheter ablation (CA) therapy, which is aimed at destroying arrhythmogenic tissue areas in the atria via high energy delivery through a catheter, has become a first-line treatment for AF, and it is the only treatment with a proven long-term curative effect (Kirchhof et al., 2016). However, even advanced CA procedures have suboptimal long-term outcomes in patients with chronic forms of AF: over half of the patients return for additional treatment within three years (Calkins et al., 2017). This can be explained by the highly empirical nature of CA therapy, which targets "usual suspect" areas without knowledge of the underlying mechanisms. It is believed that ectopic electrical beats from the pulmonary veins (PV) can trigger AF, and that re-entrant drivers (RDs) generated by breakdown of such ectopic waves can sustain AF. However, empirical CA therapy based on electrical isolation of the pulmonary veins (PV) has low success rates in chronic AF patients, where extensive ablation of remodelled non-PV areas is commonly applied (Roten et al., 2012). This warrants the development of novel approaches that can improve the efficacy of CA therapy and clinical outcomes in a large patient population.

心房颤动 (AF) 的患病率呈流行趋势：全世界有超过 3300 万人患有 AF（Chugh 等人，2014 年）。该疾病与发病率和死亡率增加、发生心力衰竭和中风的高风险有关，因此患者住院率非常高。 AF 的总体经济负担占英国医疗保健总成本的 1% 以上。维持窦性心律的节律控制策略（例如抗心律失常药物）可以显着改善心输出量和生活质量。然而，房颤的治疗因其自我维持机制而变得复杂，例如房颤引起的电和结构重塑，从而产生更多的难治性心律失常（Nattel 和 Harada，2014）。射频导管消融（CA）疗法旨在通过导管输送高能量来破坏心房内的致心律失常组织区域，已成为房颤的一线治疗方法，也是唯一被证明具有长期疗效的治疗方法（Kirchhof等，2016）。然而，即使是先进的 CA 手术，对于慢性 AF 患者来说，其长期结果也不是最理想的：超过一半的患者在三年内返回接受额外治疗（Calkins 等，2017）。这可以通过 CA 疗法的高度经验性质来解释，该疗法针对“通常可疑”的区域，但不了解其潜在机制。据信，来自肺静脉 (PV) 的异位电搏动可以触发 AF，而这种异位波分解产生的折返驱动器 (RD) 可以维持 AF。然而，基于肺静脉 (PV) 电隔离的经验性 CA 治疗在慢性 AF 患者中成功率较低，通常对重塑的非 PV 区域进行广泛消融 (Roten 等，2012)。这就需要开发新的方法来提高 CA 治疗的疗效和大量患者的临床结果。