There have been impressive advances in the physical and mathematical modelling of complex physiological systems in the last few decades, with the potential to revolutionise personalised healthcare with patient-specific evidence-based diagnosis, risk assessment and treatment decision support using digital twins. However, practical progress and genuine clinical impact hinge on successful model calibration, parameter estimation and uncertainty quantification, which calls for novel innovative adaptions and methodological extensions of contemporary state-of-the-art inference techniques from Statistics and Machine Learning. In the present study, we focus on two computational fluid-dynamics (CFD) models of the blood systemic and pulmonary circulation. We discuss state-of-the-art emulation techniques based on deep learning and Gaussian processes, which are coupled with established inference techniques based on greedy optimisation, simulated annealing, Markov Chain Monte Carlo, History Matching and rejection sampling for computationally fast inference of unknown parameters of the CFD models from blood flow and pressure data. The inference task was set as a competitive challenge which the participants had to conduct within a limited time frame representative of clinical requirements. The performance of the methods was assessed independently and objectively by the challenge organisers, based on a ground truth that was unknown to the method developers. Our results indicate that for the systemic challenge, in which an idealised case of noise-free data was considered, the relative deviation from the ground-truth in parameter space ranges from % (highest-performing method) to 3% (lowest-performing method). For the pulmonary challenge, for which noisy data was generated, the performance ranges from 0.9% to 7% deviation for the parameter posterior mean, and from 35% to 570% deviation for the parameter posterior variance.

中文翻译：

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秘密：计算逆向工程和翻译的统计仿真及其在医疗保健中的应用


过去几十年来，复杂生理系统的物理和数学建模取得了令人印象深刻的进步，有可能通过使用数字孪生进行针对患者的基于证据的诊断、风险评估和治疗决策支持来彻底改变个性化医疗保健。然而，实际进展和真正的临床影响取决于成功的模型校准、参数估计和不确定性量化，这需要对来自统计学和机器学习的当代最先进的推理技术进行新颖的创新适应和方法论扩展。在本研究中，我们重点关注血液循环和肺循环的两种计算流体动力学（CFD）模型。我们讨论基于深度学习和高斯过程的最先进的仿真技术，这些技术与基于贪婪优化、模拟退火、马尔可夫链蒙特卡罗、历史匹配和拒绝采样的现有推理技术相结合，以实现未知的计算快速推理根据血流和压力数据计算 CFD 模型的参数。推理任务被设置为一项竞争性挑战，参与者必须在代表临床要求的有限时间内完成。挑战组织者根据方法开发人员未知的基本事实独立、客观地评估了方法的性能。我们的结果表明，对于系统性挑战，其中考虑了无噪声数据的理想情况，参数空间中与真实值的相对偏差范围从 %（最高性能方法）到 3%（最低性能方法） ）。 对于生成噪声数据的肺部挑战，参数后验平均值的性能偏差为 0.9% 至 7%，参数后验方差的偏差为 35% 至 570%。