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保健基础设施提供了绝佳机会。在目前的工作中，提出了一种使用逆向分析启用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逆分析的准确解析解。然而，这些方法对于非线性系统失败或无效，例如涉及高度非线性的心血管系统（体循环）中的血流。为了解决这个问题，这项工作中提出了一种使用循环神经网络对心血管系统进行逆向分析的方法，使用虚拟患者数据库。通过输入来自三个非侵入性血管（颈动脉、股动脉和肱动脉）的压力波形，在长短期记忆 (LSTM) 细胞的帮助下，反向计算身体各血管中的血压波形。以这种方式构建的逆向分析系统应用于使用神经网络检测腹主动脉瘤（AAA）及其严重程度。