Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature

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dc.contributor.authorBanduc, Tomas
dc.contributor.authorAzzolin, Luca
dc.contributor.authorManninger, Martin
dc.contributor.authorScherr, Daniel
dc.contributor.authorPlank, Gernot
dc.contributor.authorPezzuto, Simone
dc.contributor.authorCostabal, Francisco Sahli
dc.date.accessioned2025-01-20T16:04:20Z
dc.date.available2025-01-20T16:04:20Z
dc.date.issued2025
dc.description.abstractComputational 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 retraining 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.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.media.2024.103375
dc.identifier.eissn1361-8423
dc.identifier.issn1361-8415
dc.identifier.urihttps://doi.org/10.1016/j.media.2024.103375
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/89713
dc.identifier.wosidWOS:001348594700001
dc.language.isoen
dc.revistaMedical image analysis
dc.rightsacceso restringido
dc.subjectHeat kernel signature
dc.subjectMachine learning
dc.subjectAtrial fibrillation
dc.subjectFibrosis
dc.subjectPatient-specific modeling
dc.titleSimulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature
dc.typeartículo
dc.volumen99
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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