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Cardiac image modelling: Breadth and depth in heart disease

机译:心脏图像建模:心脏病的广度和深度

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With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction. (C) 2016 Elsevier B.V. All rights reserved.
机译:随着大规模影像研究和大健康数据的出现以及分析,机器学习和计算图像分析方法的相应增长,现在有令人兴奋的机会可以加深我们对心脏病的机理和特征的了解。新兴的两个领域是心脏重塑的计算分析(疾病引起的形状和运动变化)以及生理学和力学的计算分析,以从无创成像中评估生物物理特性。为了获得从无症状到临床表现的心脏病发展的新信息,世界各地正在进行的许多大型队列研究都是基于无创成像技术专门设计的。这些为人口变异和疾病发展的量化提供了空前的广度。而且,对于单个患者,现在可以通过使用计算模型解释详细的成像数据来确定健康和疾病中心肌组织的生物物理特性。为了使这些人群和特定于患者的计算建模方法进一步发展,我们需要开放的基准来进行算法比较和验证,数据和算法的开放共享以及在患者管理和护理方面的临床疗效证明。人群和患者特定模型的结合将为心脏病的机制提供新的见解,尤其是心力衰竭,先天性心脏病,心肌梗塞,收缩功能障碍和舒张功能障碍的发展。 (C)2016 Elsevier B.V.保留所有权利。

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