Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function

心血管疾病是全球头号死亡原因，是世界卫生保健系统的巨大负担。心血管疾病的诊断和治疗计划通常是困难的，有时需要侵入性手术，这对患者本身就有风险。因此，人们对设计改进的无创诊断和治疗计划技术非常感兴趣。心血管疾病会影响心脏向全身泵血的能力。这种能力受到影响，因为心脏壁的运动已经被疾病过程改变，使泵送作用效率降低。心血管疾病的诊断和治疗计划通常涉及使用成像扫描仪，如超声或磁共振，以努力评估心脏的运动和隔离问题的根源。然而，在许多心血管应用中，诊断和治疗计划的成功率很差，患者因此而受苦。该项目的目的是利用复杂的成像和运动分析技术来设计新的无创心血管疾病生物标志物。该项目将使用先前用于纠正运动“问题”的运动建模技术，例如，在被成像的器官运动时，减少获得的图像中的伪影。这些技术将适用于分析运动的性质，并从中提取临床有用的信息。这种基于运动的信息将与其他多模态数据相结合，如解剖学信息、遗传信息或临床病史，以产生心血管功能的全面无创生物标志物。我们将重点关注两个临床范例应用。首先，心脏再同步治疗（CRT）患者的选择。CRT通常用于治疗心力衰竭，但30%的患者对治疗没有反应，因此不必要地进行了侵入性和危险的手术。我们的目标是设计出能够区分对CRT有反应和没有反应的患者的生物标志物。第二个应用是研究遗传变异对心脏运动模式的影响。大量的心血管疾病是遗传的。在其中的一些，如左心室肥厚，许多人在心力衰竭发展之前没有明显的症状。因此，人们对发现这些条件背后的机制非常感兴趣。我们的目标是设计生物标记物，帮助我们了解基因和心力衰竭之间的联系。这样的理解将有可能改善对有心力衰竭遗传风险的患者的筛查和诊断。该项目非常新颖，具有重大的潜在影响。除了上述两种临床范例应用外，如果成功，类似的技术还可以应用于其他心血管疾病，从而改善对各种心脏病的诊断和治疗。

Cardiovascular disease is the number one cause of death globally and represents a huge burden on the healthcare systems of the world. Diagnosis and planning of treatment for cardiovascular disease is often difficult and sometimes requires an invasive procedure which can itself be risky for the patient. Therefore, there is a lot of interest in devising improved and noninvasive techniques for diagnosis and treatment planning.Cardiovascular disease affects the ability of the heart to pump blood around the body. This ability is affected because the motion of the heart walls has been changed by the disease process to make the pumping action less efficient. Diagnosis and treatment planning for cardiovascular disease typically involves the use of imaging scanners such as ultrasound or magnetic resonance in an effort to evaluate the heart's motion and isolate the source of the problem. However, still in many cardiovascular applications the success rate of diagnosis and treatment planning is poor and patients suffer as a result.The aim of this project is to use sophisticated imaging and motion analysis techniques to devise novel noninvasive biomarkers for cardiovascular disease. The project will use motion modelling techniques that have previously been applied to correct the 'problem' of motion, for example to reduce artefacts in acquired images where the organ being imaged was moving. These techniques will be adapted to analyse the nature of the motion and to extract clinically useful information from it. This motion-based information will be combined with other multimodal data, such as anatomical information, genetic information or clinical history, to produce comprehensive noninvasive biomarkers of cardiovascular function.We will focus on two clinical exemplar applications. First, selection of patients for cardiac resynchronisation therapy (CRT). CRT is commonly used to treat heart failure but 30% of patients do not respond to the treatment and therefore undergo the invasive and risky procedure unnecessarily. We aim to devise biomarkers that can distinguish between patients that will respond to CRT and those that will not. The second application is the investigation of the effect of genetic variation on cardiac motion patterns. A large number of cardiovascular diseases are inherited. In several of them, such as left ventricular hypertrophy, many people exhibit no detectable symptoms until heart failure develops. Therefore, there is significant interest in discovering the mechanisms behind these conditions. We aim to devise biomarkers that can help us to understand the link between genetics and heart failure. Such an understanding would have the potential to result in improved screening and diagnosis of patients at genetic risk of heart failure.The project is highly novel and has significant potential impact. As well as the two clinical exemplar applications mentioned above, if successful similar techniques could be applied to other cardiovascular diseases, resulting in improved diagnosis and treatment for a wide range of heart conditions.