Computational models of atrial fibrillation (AF) can help improve success rates of interventions, such as ablation. However, evaluating the efficacy of different treatments requires performing multiple costly simulations by pacing at different points and checking whether AF has been induced or not, hindering the clinical application of these models. In this work, we propose a classification method that can predict AF inducibility in patient-specific cardiac models without running additional simulations. Our methodology does not require re-training when changing atrial anatomy or fibrotic patterns. To achieve this, we develop a set of features given by a variant of the heat kernel signature that incorporates fibrotic pattern information and fiber orientations: the fibrotic kernel signature (FKS). The FKS is faster to compute than a single AF simulation, and when paired with machine learning classifiers, it can predict AF inducibility in the entire domain. To learn the relationship between the FKS and AF inducibility, we performed 2371 AF simulations comprising 6 different anatomies and various fibrotic patterns, which we split into training and a testing set. We obtain a median F1 score of 85.2% in test set and we can predict the overall inducibility with a mean absolute error of 2.76 percent points, which is lower than alternative methods. We think our method can significantly speed-up the calculations of AF inducibility, which is crucial to optimize therapies for AF within clinical timelines. An example of the FKS for an open source model is provided in https://github.com/tbanduc/FKS_AtrialModel_Ferrer.git.

中文翻译：

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使用纤维化内核特征对心房颤动诱导性进行无模拟预测


心房颤动 （AF） 的计算模型可以帮助提高消融术等干预措施的成功率。然而，评估不同治疗的疗效需要通过在不同点起搏并检查是否诱发 AF 来进行多次昂贵的模拟，这阻碍了这些模型的临床应用。在这项工作中，我们提出了一种分类方法，可以在患者特异性心脏模型中预测 AF 的诱导性，而无需运行额外的模拟。我们的方法在改变心房解剖结构或纤维化模式时不需要重新训练。为了实现这一目标，我们开发了一组由热核特征的变体给出的特征，该变体结合了纤维化模式信息和纤维取向：纤维化核特征 （FKS）。FKS 的计算速度比单个 AF 模拟更快，并且当与机器学习分类器配对时，它可以预测整个域中的 AF 诱导性。为了了解 FKS 和 AF 诱导性之间的关系，我们进行了 2371 次 AF 模拟，包括 6 种不同的解剖结构和各种纤维化模式，我们将其分为训练集和测试集。我们在测试集中获得了 85.2% 的中位 F1 分数，我们可以预测整体诱导率，平均绝对误差为 2.76% 分，低于其他方法。我们认为我们的方法可以显着加快 AF 诱导性的计算，这对于在临床时间表内优化 AF 治疗至关重要。https://github.com/tbanduc/FKS_AtrialModel_Ferrer.git 中提供了开源模型的 FKS 示例。