用稀疏数据重建房颤全局动态，提升个性化治疗可能。
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
- 基于图循环网络从10%表面数据重建全心房电活动
- 相位奇点追踪性能提升一个数量级，误差降低210%
- 适合临床电生理医生用于房颤个体化诊疗分析
房颤消融治疗目前多为通用方案，对持续性房颤效果有限，可能源于传统接触式标测导管分辨率与覆盖范围不足，难以全面映射房颤动力学，制约了个性化分型。本文提出FibMap，一种图循环神经网络模型，可从稀疏测量中重建全心房房颤动态。在51例非接触式全心房记录上训练验证，仅需10%表面覆盖即可实现全心房重建，相较基线方法均方绝对误差降低210%，相位奇点追踪性能提升一个数量级。在真实接触式标测数据上验证，重建保真度接近非接触式映射。FibMap的状态空间与患者特异性参数为房颤电表型分析提供新视角。该模型有望融入临床实践，推动房颤个体化治疗与疗效提升。
原文摘要 · Abstract (English)
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and coverage provided by sequential contact mapping catheters, preventing effective patient phenotyping for personalised, targeted ablation. Here we introduce FibMap, a graph recurrent neural network model that reconstructs global AF dynamics from sparse measurements. Trained and validated on 51 non-contact whole atria recordings, FibMap reconstructs whole atria dynamics from 10% surface coverage, achieving a 210% lower mean absolute error and an order of magnitude higher performance in tracking phase singularities compared to baseline methods. Clinical utility of FibMap is demonstrated on real-world contact mapping recordings, achieving reconstruction fidelity comparable to non-contact mapping. FibMap's state-spaces and patient-specific parameters offer insights for electrophenotyping AF. Integrating FibMap into clinical practice could enable personalised AF care and improve outcomes.
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