Muizniece Laila, Bertagnoli Adrian, Qureshi Ahmed, Zeidan Aya, Roy Aditi, Muffoletto Marica, Aslanidi Oleg
School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
Department of Biomedical Engineering, ETH Zürich, Zürich, Switzerland.
Front Physiol. 2021 Aug 25;12:733139. doi: 10.3389/fphys.2021.733139. eCollection 2021.
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.
心房颤动（AF）是最常见的心律失常，仅在英国目前就影响着超过65万人。导管消融（CA）是唯一具有长期治愈效果的房颤治疗方法，因为它涉及破坏心房中的致心律失常组织。然而，其成功率并不理想，2年随访后的成功率约为50%，这种高房颤复发率需要显著改善。CA手术的图像引导已显示出临床前景，能够识别心房组织的关键患者解剖和病理（如纤维化）特征，而这些特征需要进行消融。然而，后一种方法仍然缺乏功能信息，并且需要临床医生对图像中的结构进行解读。深度学习在生物医学中发挥着越来越重要的作用，有助于高效诊断和治疗临床问题。本研究将深度强化学习与患者成像（提供心房的结构信息）和基于图像的建模（提供功能信息）相结合，设计针对患者的CA策略，以指导临床医生并提高治疗成功率。为实现这一目标，从房颤患者的延迟钆增强（LGE）磁共振成像扫描中获取针对患者的二维左心房（LA）模型，并用于模拟针对患者的房颤情况。然后创建了一种强化Q学习算法，其中一个消融智能体在二维LA周围移动，应用CA损伤来终止房颤，并通过奖励策略施加的反馈进行学习。该智能体在训练期间终止房颤的成功率达到84%，在测试中的成功率为72%。最后，通过在CA后尝试在二维心房模型中重新引发房颤来测量房颤复发率，11%的复发率表明与现有疗法相比有了很大改善。因此，强化Q学习算法可以从患者的MRI数据预测成功的CA策略，并有助于改善CA治疗的针对患者的指导。
