Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attractive compromise between computational efficiency and solution fidelity, their application on large populations or for generating large \emph{in silico} cohorts remains challenging. Certain hemodynamic parameters like the terminal resistance/compliance, are difficult to clinically estimate and often yield non-physiological hemodynamics when sampled naively, resulting in large portions of simulated datasets to be discarded. In this work, we present a systematic framework for training machine learning (ML) models, capable of instantaneous hemodynamic prediction and parameter estimation. We initially start with generating a parametric virtual cohort of patients which is based on the multivariate correlations observed in the large Asklepios clinical dataset, ensuring that physiological parameter distributions are respected. We then train a deep neural surrogate model, able to predict patient-specific arterial pressure and cardiac output (CO), enabling rapid a~priori screening of input parameters. This allows for immediate rejection of non-physiological combinations and drastically reduces the cost of targeted synthetic dataset generation (e.g. hypertensive groups). The model also provides a principled means of sampling the terminal resistance to minimize the uncertainties of unmeasurable parameters. Moreover, by assessing the model's predictive performance we determine the theoretical information which suffices for solving the inverse problem of estimating the CO. Finally, we apply the surrogate on a clinical dataset for the estimation of central aortic hemodynamics i.e. the CO and aortic systolic blood pressure (cSBP).

翻译：心血管建模在过去几十年中因健康追踪和心血管疾病早期检测需求的增长而迅速发展。尽管一维动脉模型在计算效率与解精度之间提供了有吸引力的折衷方案，但其在庞大人群中的应用或生成大规模计算虚拟队列仍具挑战性。某些血流动力学参数（如末端阻力/顺应性）难以通过临床方法估算，且当采用朴素采样时，常产生非生理性血流动力学特征，导致模拟数据集中大量样本被丢弃。本研究提出了一套系统化框架，用于训练能够实现瞬时血流动力学预测与参数估计的机器学习模型。我们首先基于大型阿斯克勒皮俄斯临床数据集中的多元相关性，生成参数化虚拟患者队列，确保生理参数分布得到遵循。随后训练了一种深度神经替代模型，该模型能够预测患者特异性动脉压和心输出量，从而实现对输入参数的快速先验筛选。该方法可即时排除非生理参数组合，并大幅降低目标定向合成数据集（如高血压组）的生成成本。该模型还为末端阻力采样提供了规范化方法，以最小化不可测量参数的不确定性。此外，通过评估模型预测性能，我们确定了足以解决心输出量估计逆问题的理论信息。最后，我们将该替代模型应用于临床数据集，用于估算中心主动脉血流动力学参数，即心输出量和主动脉收缩压。