The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. Here, we propose a probabilistic approach for identifying the Purkinje network from non-invasive clinical data such as the standard electrocardiogram (ECG). We use cardiac imaging to build an anatomically accurate model of the ventricles; we algorithmically generate a rule-based Purkinje network tailored to the anatomy; we simulate physiological electrocardiograms with a fast model; we identify the geometrical and electrical parameters of the Purkinje-ECG model with Bayesian optimization and approximate Bayesian computation. The proposed approach is inherently probabilistic and generates a population of plausible Purkinje networks, all fitting the ECG within a given tolerance. In this way, we can estimate the uncertainty of the parameters, thus providing reliable predictions. We test our methodology in physiological and pathological scenarios, showing that we are able to accurately recover the ECG with our model. We propagate the uncertainty in the Purkinje network parameters in a simulation of conduction system pacing therapy. Our methodology is a step forward in creation of digital twins from non-invasive data in precision medicine. An open source implementation can be found at http://github.com/fsahli/purkinje-learning

翻译：心脏浦肯野传导系统的识别是一项具有挑战性的任务，但对精准心脏病学中心脏数字孪生的正确定义至关重要。本文提出了一种概率方法，用于从标准心电图（ECG）等非侵入性临床数据中识别浦肯野网络。我们利用心脏成像构建心室解剖精确模型；通过算法生成基于规则、适配该解剖结构的浦肯野网络；使用快速模型模拟生理心电图；结合贝叶斯优化与近似贝叶斯计算，识别浦肯野-ECG模型的几何和电学参数。该方法本质上是概率性的，可生成一组合理的浦肯野网络群体，所有网络均能在给定容差内拟合心电图。通过这种方式，我们能够估计参数的不确定性，从而提供可靠的预测。我们在生理和病理场景中测试了该方法，表明模型能够准确恢复心电图。我们将浦肯野网络参数的不确定性传播至传导系统起搏治疗的仿真中。本方法是从非侵入性数据创建数字孪生迈向精准医学的一步。开源实现详见 http://github.com/fsahli/purkinje-learning。