用物理约束神经网络融合多组预测，实现心房纤维方向的高精度不确定性估计。
Ensemble learning of the atrial fiber orientation with physics-informed neural networks
- 构建神经网络集成模型，从电生理数据中推断心房纤维方向与传导速度。
- 在8种不同心脏解剖结构上，纤维方向误差显著低于先前方法。
- 支持临床应用，可在7分钟内完成估计并提供量化不确定性。
心肌各向异性结构是决定心脏功能的关键因素。目前尚无成像技术可实现活体心脏纤维结构的评估。我们此前提出Fibernet方法，利用电生理标测记录的心脏激活数据，通过物理信息神经网络自动识别心房中的各向异性传导（即纤维方向）。本文进一步扩展Fibernet，以应对纤维场估计中的不确定性。具体地，采用神经网络集成生成多个符合观测数据的样本，并计算后验统计量；同时引入新方法，直接在心房表面定义输入，优化模型性能。改进后的方法在8种不同心房解剖结构上均显著降低纤维方向误差。当前，该方法可在7分钟内完成纤维方向与传导速度的估计，并提供不确定性量化，为临床应用铺平道路。我们期望该方法能推动心脏数字孪生的个性化发展，助力精准医疗。
原文摘要 · Abstract (English)
The anisotropic structure of the myocardium is a key determinant of the cardiac function. To date, there is no imaging modality to assess in-vivo the cardiac fiber structure. We recently proposed Fibernet, a method for the automatic identification of the anisotropic conduction -- and thus fibers -- in the atria from local electrical recordings. Fibernet uses cardiac activation as recorded during electroanatomical mappings to infer local conduction properties using physics-informed neural networks. In this work, we extend Fibernet to cope with the uncertainty in the estimated fiber field. Specifically, we use an ensemble of neural networks to produce multiple samples, all fitting the observed data, and compute posterior statistics. We also introduce a methodology to select the best fiber orientation members and define the input of the neural networks directly on the atrial surface. With these improvements, we outperform the previous methodology in terms of fiber orientation error in 8 different atrial anatomies. Currently, our approach can estimate the fiber orientation and conduction velocities in under 7 minutes with quantified uncertainty, which opens the door to its application in clinical practice. We hope the proposed methodology will enable further personalization of cardiac digital twins for precision medicine.
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