Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Swansea, SA2 8PP, UK.
Biomech Model Mechanobiol. 2021 Apr;20(2):449-465. doi: 10.1007/s10237-020-01393-6. Epub 2020 Oct 16.
An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks.
患者数据的指数级增长为改善现有医疗基础设施提供了绝佳机会。在本工作中，使用反演分析提出了一种实现心血管数字孪生的方法。传统上，已经提出并使用了用于线性问题反演分析的精确解析解。然而，对于涉及高度非线性的非线性系统（如心血管系统（体循环）中的血流），这些方法失败或效率不高。为了解决这个问题，本工作提出了一种使用递归神经网络进行心血管系统反演分析的方法，使用虚拟患者数据库。通过从三个非侵入性可访问的血管（颈动脉、股动脉和肱动脉）输入压力波形，使用长短期记忆（LSTM）细胞反向计算身体各部位血管的血压波形。以这种方式构建的反演分析系统被应用于使用神经网络检测腹主动脉瘤（AAA）及其严重程度。
