Predicting Atrial Fibrillation Treatment Outcome with Siamese Multi-modal Fusion and Cardiac Digital Twins

Alexander M. Zolotarev, Abbas Khan Rayabat Khan, Gregory Slabaugh, Caroline Roney
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:1927-1938, 2024.

Abstract

Atrial fibrillation, the most common heart rhythm disorder, presents challenges in treatment due to difficulty pinpointing the patient-specific regions of abnormal electrical activity. While biophysical simulations of cardiac electrophysiology create a digital twin of atrial electrical activity based on CT or MRI scans, testing various treatment strategies on them is time-consuming and impractical on clinical timescales.Our proposed pipeline, incorporating Siamese architecture, fuses latent representations of multi-modal features extracted from atrial digital twin before any therapy and predicts the outcomes of several treatment strategies.A large in-silico dataset of 1000 virtual patients, generated from clinical data, was utilized to provide the biophysical simulations before (used for feature extraction) and after (used for calculating ground truth labels depending on whether atrial fibrillation terminates or not) various treatment strategies. By accurately predicting freedom from atrial fibrillation, our pipeline paves the way for personalized atrial fibrillation therapy with a fast and precise selection of optimal treatments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-zolotarev24a, title = {Predicting Atrial Fibrillation Treatment Outcome with Siamese Multi-modal Fusion and Cardiac Digital Twins}, author = {Zolotarev, Alexander M. and Khan, Abbas Khan Rayabat and Slabaugh, Gregory and Roney, Caroline}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {1927--1938}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/zolotarev24a/zolotarev24a.pdf}, url = {https://proceedings.mlr.press/v250/zolotarev24a.html}, abstract = {Atrial fibrillation, the most common heart rhythm disorder, presents challenges in treatment due to difficulty pinpointing the patient-specific regions of abnormal electrical activity. While biophysical simulations of cardiac electrophysiology create a digital twin of atrial electrical activity based on CT or MRI scans, testing various treatment strategies on them is time-consuming and impractical on clinical timescales.Our proposed pipeline, incorporating Siamese architecture, fuses latent representations of multi-modal features extracted from atrial digital twin before any therapy and predicts the outcomes of several treatment strategies.A large in-silico dataset of 1000 virtual patients, generated from clinical data, was utilized to provide the biophysical simulations before (used for feature extraction) and after (used for calculating ground truth labels depending on whether atrial fibrillation terminates or not) various treatment strategies. By accurately predicting freedom from atrial fibrillation, our pipeline paves the way for personalized atrial fibrillation therapy with a fast and precise selection of optimal treatments.} }
Endnote
%0 Conference Paper %T Predicting Atrial Fibrillation Treatment Outcome with Siamese Multi-modal Fusion and Cardiac Digital Twins %A Alexander M. Zolotarev %A Abbas Khan Rayabat Khan %A Gregory Slabaugh %A Caroline Roney %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-zolotarev24a %I PMLR %P 1927--1938 %U https://proceedings.mlr.press/v250/zolotarev24a.html %V 250 %X Atrial fibrillation, the most common heart rhythm disorder, presents challenges in treatment due to difficulty pinpointing the patient-specific regions of abnormal electrical activity. While biophysical simulations of cardiac electrophysiology create a digital twin of atrial electrical activity based on CT or MRI scans, testing various treatment strategies on them is time-consuming and impractical on clinical timescales.Our proposed pipeline, incorporating Siamese architecture, fuses latent representations of multi-modal features extracted from atrial digital twin before any therapy and predicts the outcomes of several treatment strategies.A large in-silico dataset of 1000 virtual patients, generated from clinical data, was utilized to provide the biophysical simulations before (used for feature extraction) and after (used for calculating ground truth labels depending on whether atrial fibrillation terminates or not) various treatment strategies. By accurately predicting freedom from atrial fibrillation, our pipeline paves the way for personalized atrial fibrillation therapy with a fast and precise selection of optimal treatments.
APA
Zolotarev, A.M., Khan, A.K.R., Slabaugh, G. & Roney, C.. (2024). Predicting Atrial Fibrillation Treatment Outcome with Siamese Multi-modal Fusion and Cardiac Digital Twins. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:1927-1938 Available from https://proceedings.mlr.press/v250/zolotarev24a.html.

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