Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.

中文翻译：

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使用真实的浦肯野网络将人体心室激动序列与临床 12 导联心电图和磁共振成像进行数字孪生，用于计算机模拟临床试验

心脏计算机模拟临床试验可以使用基于人体的建模和模拟来虚拟评估治疗的安全性和有效性。这些技术可以为临床观察到的病理行为提供机制解释。为计算机试验设计虚拟队列需要利用临床数据来捕获人群的生理变异性。心室激动和浦肯野网络的临床特征具有挑战性，尤其是非侵入性的。我们的研究旨在提出一种新颖的数字孪生管道，该管道可以基于特定主题的临床 12 导联心电图和磁共振记录，有效生成浦肯野网络并将其集成到人类多尺度双心室模型中。该管道的基本新颖特征是基于人类的浦肯野网络生成方法、考虑心电图 R 波进展以及 QRS 形态的个性化，以及从降阶 Eikonal 模型到等效的生物物理详细单域模型的转换。我们在四名对照受试者和两名肥厚型心肌病患者中使用基于临床图像的浦肯野病多尺度模型证明了符合临床数据的心电图模拟（模拟和临床 QRS 波群，皮尔逊相关系数 > 0.7）。我们的方法还考虑了基于 Ekonal 的推理中浦肯野心肌连接密度的可能差异作为区域传导速度。这些差异转化为单域配方中浦肯野模型和心肌模型之间的区域耦合效应。总之，我们展示了一个数字孪生管道，能够通过基于临床 CMR 图像的双心室多尺度模型进行模拟，产生临床一致的心电图，包括健康和心脏病条件下的个性化浦肯野病。