Image-Based Stratification and Therapy Selection for Atrial Fibrillation Patients Using Deep Learning

该项目的目标：开发基于图像的深度学习模型，用于对房颤（AF）患者进行分层。应用这些模型识别对导管消融治疗无反应的AF患者。应用这些模型识别需要抗凝治疗的卒中高风险AF患者。使用临床结局和患者随访的可用数据进行预测。房颤（AF）是最常见的心律失常，影响全世界超过5000万人，占缺血性中风的三分之一。其诊断和管理给医疗保健系统带来了巨大负担，阻碍了对AF患者治疗选择和卒中风险进行分层的有效临床方法。然而，即使是先进的房颤治疗，如导管消融术（CA），也是高度经验性的，长期结局不佳，约有一半的患者会复发心律失常。卒中风险评估的临床方法也是经验性的，基于患者特征（如年龄、体重）和合并症，对高危AF患者大多有效。这就需要开发新的、可靠的方法来对AF患者进行分层，这些方法可以解释来自成像和临床检查的解剖和功能指标。因此，使用MRI在左心房（LA）中识别的纤维化组织已被用作AF严重程度的生物标志物和CA治疗的靶点。通过捕获LA形状、运动或血流的多模式心脏成像也可以改善卒中风险评分，因为所有这些特征都与血液凝固和血栓形成的可能性有关。然而，即使是先进的成像系统提供的信息有限的关键因素的基础AF的支持和AF相关的血栓形成，和成功的AF患者的图像为基础的分层仍然是次优的。此外，分析大量的患者成像数据需要大量的人力和计算资源，这阻碍了其在临床环境中的应用。深度学习（DL）的应用可以帮助克服这些限制：（i）可以使用来自多个来源的数据来训练DL模型，包括患者成像，基于图像的建模，临床记录和离体实验;（ii）一旦训练完成，DL模型可以提供一个快速工具，以支持基于可用患者数据的临床决策。因此，该项目将开发新的DL模型，这些模型是根据AF患者的成像、建模和临床数据进行训练的。一旦经过训练，这些模型将提供快速灵活的工具，以识别1）不太可能对CA治疗有反应的AF患者和2）需要抗凝治疗的高血栓形成风险的AF患者。这些预测将根据患者提供的临床随访数据进行验证。

Aims of the Project: Develop image-based deep learning models for stratification of atrial fibrillation (AF) patients.Apply the models to identify AF patients who would not respond to catheter ablation therapy.Apply the models to identify AF patients with high risks of stroke who require anticoagulation.Validate predictions using available data on clinical outcomes and follow-up in patients.Atrial fibrillation (AF), the most common arrhythmia, affects over 50 million people worldwide and accounts for a third of ischaemic strokes. Its diagnosis and management pose a substantial burden on healthcare systems, warranting efficient clinical approaches for stratifying AF patient therapy selection and stroke risks. However, even advanced AF therapies, such as catheter ablation (CA), are highly empirical and have poor long-term outcomes, with arrhythmia recurring in about half of the patients. Clinical approaches to stroke risk assessment are also empirical, based on patient characteristics (such as age, weight) and comorbidities, and are mostly effective for high-risk AF patients. This warrants the development of novel, reliable approaches to AF patient stratification that can account for anatomical and functional metrics from imaging and clinical exams.Image-based assessments are increasingly used to move away from empirical diagnosis and therapy and improve patient outcomes. Thus, fibrotic tissue identified in the left atrium (LA) using MRI has been used as both a biomarker of AF severity and a target for CA therapy. Stroke risk scores may also be improved by multi-modal cardiac imaging that capture LA shape, motion or blood flow, since all these characteristics are linked with the likelihood of blood coagulation and thrombus formation. However, even advanced imaging systems provide limited information on key factors underlying AF sustenance and AF-related thrombogenesis, and the success of image-based stratification of AF patients remains suboptimal. Moreover, analysis of large volumes of patient imaging data requires substantial human and computational resources, which hinder their application in a clinical setting.The application of deep learning (DL) can help overcome such limitations: (i) DL models can be trained using data from multiple sources, including patient imaging, image-based modelling, clinical records and ex-vivo experiments; (ii) once trained, DL models can provide a fast tool to support clinical decision-making based on available patient data. Therefore, this project will develop novel DL models trained on a combination of imaging, modelling and clinical data from AF patients. Once trained, the models will provide fast and flexible tools to identify 1) AF patients who are unlikely to respond to CA therapy and 2) AF patients with high thrombogenic risks who require anticoagulation. These predictions will be validated against clinical follow-up data available from the patients.