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

使用深度学习对心房颤动患者进行基于图像的分层和治疗选择

• 批准号: 2886591
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886591
• 关键词: Image; Based; Stratification; Therapy; Selection

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.

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全球，占缺血性中风的三分之一。它的诊断和管理对医疗保健系统造成了重大负担，保证有效的临床方法来分层AF患者治疗选择和中风风险。但是，即使是高级AF疗法，例如导管消融（CA），也具有高度的经验性，长期结局差，大约一半的患者也会反复出现心律不齐。基于患者特征（例如年龄，体重）和合并症，对中风风险评估的临床方法也是经验性的，并且对高危AF患者最有效。这需要开发新颖，可靠的AF患者分层方法，这些方法可以解释来自成像和临床检查的解剖学和功能指标。基于图像的评估越来越多地用于摆脱经验诊断和治疗，并改善患者的结果。因此，使用MRI在左心房（LA）中鉴定出的纤维化组织已被用作AF严重程度的生物标志物，又是CA治疗的靶标。捕获LA形状，运动或血流的多模式心脏成像也可以改善中风风险评分，因为所有这些特征都与血液凝结和血栓形成的可能性有关。但是，即使是先进的成像系统也提供了有关自动房屋维持和与AF相关的血栓形成基础的关键因素的有限信息，而基于图像的AF患者基于图像的分层的成功仍然是最佳的。此外，对大量患者成像数据的分析需要大量的人类和计算资源，这阻碍了它们在临床环境中的应用。深度学习的应用（DL）可以帮助克服此类限制：（i）可以使用来自多个来源的数据来培训DL模型，包括患者成像，基于图像的建模，临床记录和Ex-Vivo实验； （ii）一旦受过培训，DL模型就可以提供基于可用患者数据的临床决策的快速工具。因此，该项目将开发新型的DL模型，该模型以AF患者的成像，建模和临床数据的结合进行了培训。一旦受过培训，这些模型将提供快速，灵活的工具来识别1）不太可能对CA治疗做出反应的AF患者； 2）具有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获得的临床随访数据进行验证。