Purpose

Patient-specific models of the heart can be used to improve the diagnosis of cardiac diseases, but practical application of these models can be impeded by the computational costs and numerical uncertainties of fitting mechanistic models to clinical measurements from individual patients. Reliable and efficient tuning of these models within clinically appropriate error bounds is a requirement for practical deployment in the time-constrained environment of the clinic.

Methods

We developed an optimization framework to tune parameters of patient-specific mechanistic models using routinely-acquired non-invasive patient data more efficiently than manual methods. We employ a hybrid particle swarm and pattern search optimization algorithm, but the framework can be readily adapted to use other optimization algorithms.

Results

We apply the proposed framework to tune full-cycle lumped parameter circulatory models using clinical data. We show that our framework can be easily adapted to optimize cross-species models by tuning the parameters of the same circulation model to four canine subjects.

Conclusions

This work will facilitate the use of biomechanics and circulatory cardiac models in both clinical and research environments by ameliorating the tedious process of manually fitting the parameters.

中文翻译：

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患者特定心脏模型的优化框架。

 目的

患者特定的心脏模型可用于改善心脏病的诊断，但这些模型的实际应用可能会因将机械模型与个体患者的临床测量相拟合的计算成本和数值不确定性而受到阻碍。在临床上适当的误差范围内可靠且高效地调整这些模型是在诊所时间有限的环境中实际部署的要求。

 方法

我们开发了一个优化框架，使用常规获取的非侵入性患者数据比手动方法更有效地调整患者特定机械模型的参数。我们采用混合粒子群和模式搜索优化算法，但该框架可以很容易地适应使用其他优化算法。

 结果

我们应用所提出的框架来使用临床数据调整全周期集总参数循环模型。我们表明，通过将同一循环模型的参数调整到四个犬科动物，我们的框架可以轻松适应优化跨物种模型。

 结论

这项工作将通过改善手动拟合参数的繁琐过程，促进生物力学和循环心脏模型在临床和研究环境中的使用。