Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies.
Overview
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Computational Medicine Program, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
- McAllister Heart Institute, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
Abstract
One in five patients diagnosed with atrial fibrillation die within a year after being diagnosed according to a prospective cohort study done in the United Kingdom, making this disease the focus of many scientific studies. One approach to studying this disease has been computational models since they have demonstrated powerful capabilities in understanding and analyzing the biochemical processes underlying atrial fibrillation. To create accurate patient models for studying cardiovascular diseases, we developed a pipeline for generating patient-specific models of the left atrial posterior wall using point cloud data without image segmentation. Our goal was to evaluate the performance of these models by comparing simulated electrograms to those obtained from atrial fibrillation patients. We created models for two different paroxysmal atrial fibrillation patients under healthy tissue conditions. To validate our model, we compared simulated and measured electrograms using various metrics. Some electrograms matched well in terms of local activation time and cross-correlation peak, whereas others showed significant differences in amplitude and duration. Additionally, we explored the impact of modeling fibrotic tissue on electrogram morphology by creating four persistent atrial fibrillation patient models with varying fibrosis densities and types. Simulations indicated that increased modeled fibrosis density led to more multiphasic electrogram morphologies, with little impact from fibrosis type. The fibrosis simulations also had morphological characteristics seen in other fibrosis electrophysiology modeling studies like deflections patterns and amplitudes, strengthening the reasoning behind using this type of model generation methodology. Our findings suggest that point cloud data is sufficient for creating accurate left atrial posterior wall models, which can simulate electrograms comparable to measured waveforms. This method could be useful for patient-specific studies, potential specialized ablation procedures, and arrhythmia research.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper links to its data and to no code of its authors: see the Data section.
Tracing map
A tracing map links a paper to the code its authors published: this paper has none, so it has no map.
Data
Datasets cited
- doi:10.5061/
dryad.c866t1gn7 , at Dryad; found in DataCite
Data Availability
The data underlying the results presented in the study are available from Dryad at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 10 MeSH terms, 2 funders, 87 references.
Cite
This paper
Nataren Moran, J., Abdala, L., & Griffith, B. E. (2026). Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies. PloS one, 21(3), e0344274. https://
BibTeX
@article{natarenmoran202
author = {Nataren Moran, Josue and Abdala, Laryssa and Griffith, Boyce E.},
title = {{Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies}},
journal = {PloS one},
year = {2026},
month = mar,
volume = {21},
number = {3},
pages = {e0344274},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/
url = {https://
pmid = {41886529},
pmcid = {PMC13020965}
}
RIS
TY - JOUR
AU - Nataren Moran, Josue
AU - Abdala, Laryssa
AU - Griffith, Boyce E.
TI - Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/
VL - 21
IS - 3
SP - e0344274
SN - 1932-6203
PB - PLOS
DO - 10.1371/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.1371/
"type": "article-journal",
"title": "Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies",
"container-title": "PloS one",
"author": [
{
"family": "Nataren Moran",
"given": "Josue"
},
{
"family": "Abdala",
"given": "Laryssa"
},
{
"family": "Griffith",
"given": "Boyce E."
}
],
"container-title-short":
"volume": "21",
"issue": "3",
"page": "e0344274",
"DOI": "10.1371/
"PMID": "41886529",
"PMCID": "PMC13020965",
"ISSN": "1932-6203",
"publisher": "PLOS",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
26
]
]
}
}
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.3389/fphys.2022.912947 [code]
- Rule-based definition of muscle bundles in patient-specific models of the left atrium.Journal: Frontiers in physiologyIn common: 8 references
- [2] doi:10.3389/fphys.2024.1403545 [code]
- Re-entry in models of cardiac ventricular tissue with scar represented as a Gaussian random field.Journal: Frontiers in physiologyIn common: 3 references
- [3] doi:10.1016/j.cnsns.2022.106794 [code]
- Homogenisation for the monodomain model in the presence of microscopic fibrotic structures.Journal: Communications in nonlinear science & numerical simulationIn common: 3 references
- [4] doi:10.1186/s12859-023-05513-8 [code]
- lifex-ep: a robust and efficient software for cardiac electrophysiology simulations.Journal: BMC bioinformaticsIn common: 2 references
- [5] doi:10.3390/bioengineering12101102
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Journal: Bioengineering (Basel, Switzerland)In common: 1 reference
- [6] doi:10.1371/journal.pcbi.1014325 [code]
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.Journal: PLoS computational biologyIn common: 1 reference
- [7] doi:10.1002/nbm.70038 [code]
- WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.Journal: NMR in biomedicineIn common: 1 reference
- [8] doi:10.1038/s41597-023-02416-4 [code]
- MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.Journal: Scientific dataIn common: 1 reference
- [9] doi:10.1038/s41598-025-33709-w [code]
- Toward cardiac electrophysiology digital twins with an efficient open source scalable solver on GPU clusters.Journal: Scientific reportsIn common: 1 reference
- [10] doi:10.1101/2025.04.09.647733 [code]
- MonoAlg3D: Enabling Cardiac Electrophysiology Digital Twins with an Efficient Open Source Scalable Solver on GPU ClustersJournal: bioRxiv (preprint)In common: 1 reference
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.
Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.