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

使用机器学习识别心脏功能的无创基于运动的生物标志物

• 批准号: EP/K030310/1
• 负责人: Andrew King
• 金额: $ 36.74万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK030310%2F1
• 关键词: Using; Machine; Learning; Identify; Noninvasive

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.

心血管疾病是全球第一大死因，给世界医疗保健系统带来巨大负担。心血管疾病的诊断和治疗计划通常很困难，有时需要侵入性手术，这本身对患者来说可能是有风险的。因此，人们对设计用于诊断和治疗计划的改进的非侵入性技术非常感兴趣。心血管疾病影响心脏将血液泵送到全身的能力。这种能力受到影响，因为疾病过程改变了心壁的运动，导致泵血作用效率降低。心血管疾病的诊断和治疗计划通常涉及使用超声或磁共振等成像扫描仪来评估心脏运动并隔离问题根源。然而，在许多心血管应用中，诊断和治疗计划的成功率仍然很低，患者因此遭受痛苦。该项目的目的是使用复杂的成像和运动分析技术来设计用于心血管疾病的新型非侵入性生物标志物。该项目将使用先前用于纠正运动“问题”的运动建模技术，例如减少所采集图像中被成像器官移动时的伪影。这些技术将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（例如解剖信息、遗传信息或临床病史）相结合，产生心血管功能的综合无创生物标志物。我们将重点关注两个临床范例应用。首先，选择患者进行心脏再同步治疗（CRT）。 CRT 通常用于治疗心力衰竭，但 30% 的患者对治疗没有反应，因此不必要地接受侵入性且危险的手术。我们的目标是设计生物标志物来区分对 CRT 产生反应的患者和对 CRT 没有反应的患者。第二个应用是研究遗传变异对心脏运动模式的影响。大量心血管疾病是遗传性的。在其中一些疾病中，例如左心室肥厚，许多人在发生心力衰竭之前没有表现出任何可检测到的症状。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志物来帮助我们了解遗传学和心力衰竭之间的联系。这种理解将有可能改善对具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。除了上面提到的两个临床范例应用之外，如果成功的话，类似的技术也可以应用于其他心血管疾病，从而改善多种心脏病的诊断和治疗。

项目成果

Estimation of passive and active properties in the human heart using 3D tagged MRI.

• DOI: 10.1007/s10237-015-0748-z
• 发表时间: 2016-10
• 期刊: BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
• 影响因子: 3.5
• 作者: Asner, Liya;Hadjicharalambous, Myrianthi;Chabiniok, Radomir;Peresutti, Devis;Sammut, Eva;Wong, James;Carr-White, Gerald;Chowienczyk, Philip;Lee, Jack;King, Andrew;Smith, Nicolas;Razavi, Reza;Nordsletten, David
• 通讯作者: Nordsletten, David

Registration of Multiview Echocardiography Sequences Using a Subspace Error Metric

使用子空间误差度量注册多视图超声心动图序列

• DOI: 10.1109/tbme.2016.2550487
• 发表时间: 2017
• 期刊: IEEE Transactions on Biomedical Engineering
• 影响因子: 4.6
• 作者: Peressutti D

Hollow Gradient-Structured Iron-Anchored Carbon Nanospheres for Enhanced Electromagnetic Wave Absorption.

用于增强电磁波吸收的空心梯度结构铁锚碳纳米球。

• DOI: 10.1007/978-3-319-52718-5_7
• 发表时间: 2022
• 期刊: Nano-micro letters
• 影响因子: 26.6
• 作者: Wu C

Towards Left Ventricular Scar Localisation Using Local Motion Descriptors

• DOI: 10.1007/978-3-319-28712-6_4
• 发表时间: 2015-10
• 作者: D. Peressutti;Wenjia Bai;W. Shi;C. Tobon-Gomez;T. Jackson;M. Sohal;C. Rinaldi;D. Rueckert;A. King

Prospective Identification of CRT Super Responders Using a Motion Atlas and Random Projection Ensemble Learning

使用运动图集和随机投影集成学习对 CRT 超级响应者进行前瞻性识别

• 发表时间: 2015
• 作者: Peressutti D