Computer methods and programs in biomedicine · 2026
Graph neural network for predicting reentrant drivers in atrial digital twins.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Loeffler Shane, Lal Yash, Yee Alana, Mohsen Yazan, Sakata Kensuke, Yamamoto Carolyna, Ali Syed Yusuf, Prakosa Adityo, Kholmovski Eugene G, Trayanova Natalia A
Аннотация
BACKGROUND AND OBJECTIVE: Atrial Fibrillation (AF) is the most common cardiac arrhythmia and is associated with high risk of stroke and heart failure. While pulmonary vein isolation is a standard treatment, its efficacy is suboptimal for patients with persistent AF (PsAF), resulting in high recurrence rates. Increasing evidence indicates that fibrotic remodeling can create arrhythmogenic substrates outside the pulmonary veins, which may contribute to recurrence. Previous works have shown that PsAF Digital Twins (DT) can locate these arrhythmogenic regions and inform ablation targets for AF treatment, characterizing the full arrhythmogenic substrate through iterative simulations, including emergent reentries. While PsAF DTs are effective, their use in clinical practice is constrained by substantial computational demands.
METHODS: In this study, we developed a Graph Neural Network (GNN) to directly identify regions sustaining reentrant drivers (ablation targets) without the need of computationally expensive simulations. The GNN was trained on 31 high-resolution PsAF DTs. Afterwards, the GNN's predictions were prospectively validated on four new PsAF DTs, where the predictions served as virtual ablation targets and new simulations were performed to evaluate the GNN's predictions to eliminate reentrant drivers on new geometries and fibrotic distributions.
RESULTS: Across 8-fold cross validation, the GNN achieved an AUROC of 0.884 and an AUPRC of 0.174, both of which surpassed all other tested models. GNN's positive predictions with the greatest confidence were refined to regions with high fibrotic density and moderate fibrotic entropy. In the proof-of-concept prospective validation, GNN-guided ablation reduced emergent reentries by over 70% relative to the wide antral circumferential ablation pulmonary vein isolation (WACA PVI)-only control and reduced the mean number of additional simulation rounds required to reach non-inducibility by 66%.
CONCLUSION: This study demonstrates the potential of a GNN to guide DT-based ablation for PsAF patients and to reduce the iterative simulation burden of DT workflows. These findings support the feasibility of GNN-guided substrate prioritization, with further validation supporting clinical integration.
METHODS: In this study, we developed a Graph Neural Network (GNN) to directly identify regions sustaining reentrant drivers (ablation targets) without the need of computationally expensive simulations. The GNN was trained on 31 high-resolution PsAF DTs. Afterwards, the GNN's predictions were prospectively validated on four new PsAF DTs, where the predictions served as virtual ablation targets and new simulations were performed to evaluate the GNN's predictions to eliminate reentrant drivers on new geometries and fibrotic distributions.
RESULTS: Across 8-fold cross validation, the GNN achieved an AUROC of 0.884 and an AUPRC of 0.174, both of which surpassed all other tested models. GNN's positive predictions with the greatest confidence were refined to regions with high fibrotic density and moderate fibrotic entropy. In the proof-of-concept prospective validation, GNN-guided ablation reduced emergent reentries by over 70% relative to the wide antral circumferential ablation pulmonary vein isolation (WACA PVI)-only control and reduced the mean number of additional simulation rounds required to reach non-inducibility by 66%.
CONCLUSION: This study demonstrates the potential of a GNN to guide DT-based ablation for PsAF patients and to reduce the iterative simulation burden of DT workflows. These findings support the feasibility of GNN-guided substrate prioritization, with further validation supporting clinical integration.