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Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.

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Paper

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The authors' code

Python · 3 lines · 84 B · MIT

  1. import pkg_resources
  2. version = pkg_resources.get_distribution(__package__).version

__init__.py at commit f46ec3c, under MIT · at the source

Overview

Authors: Shambhavi Malik1, Ludovica Cicci1, Abdul Qayyum1, Rahul Ghelani2, Ji-jian Chow2, Jagdeep Singh Mohal1, Zachary I Whinnett1, Amanda Varnava2, Gernot Plank3, Prapa Kanagaratnam2, Steven A Niederer1,4
ORCID iDs: Shambhavi Malik
  1. National Heart and Lung Institute, Imperial College London, London, United Kingdom
  2. Imperial College Healthcare NHS Trust, London, United Kingdom
  3. Medical University of Graz, Graz, Austria
  4. The Alan Turing Institute, London, United Kingdom
Institutions: Imperial College London (United Kingdom); Imperial College Healthcare NHS Trust (United Kingdom); Medical University of Graz (Austria); The Alan Turing Institute (United Kingdom)
Journal: PLoS computational biology, volume 22, issue 7, article e1014555
Dates: received 6 March 2026; accepted 8 July 2026; published online 27 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014555 · PMID 42507707 · PMCID PMC13432148 · OpenAlex W7171355284
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other (modality), human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Evoked potentials, fMRI & imaging
MeSH: Body Surface Potential Mapping*, Cardiomyopathy, Hypertrophic*, Adult, Aged, Bayes Theorem, Computational Biology, Computer Simulation, Electrocardiography, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Cardiovascular, Patient-Specific Modeling (* major topic)
Topic: Cardiac electrophysiology and arrhythmias (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex
Funding: European Research Council (ERC PREDICT-HF 864055, AMD-864055-8, 864055); NHLBI NIH HHS (R01 HL152256); Engineering and Physical Sciences Research Council (NS/A000049/1, EP/W000091/2, EP/X012603/2, EP/M012492/1, EP/P01268X/1); British Heart Foundation (PG/13/37/30280, FS/CRTF/21/24171, SP/18/6/33805, PG/15/91/31812); Austrian Science Fund FWF (10.55776/I6540); Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Smart Medical Imaging (EP/S022104/1); National Institutes of Health (NIH R01-HL152256); Alan Turing Institute
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

Background: Hypertrophic cardiomyopathy (HCM) is associated with marked inter-patient heterogeneity in ventricular electrophysiology, contributing to arrhythmic risk that is insufficiently captured by current clinical methods. Electrocardiographic imaging (ECGI) provides high-density body surface potential (BSP) measurements but remains largely descriptive. Computational modelling offers a mechanistic framework to interpret BSP signals in terms of underlying tissue-level properties.

Methods and findings: We developed a BSP-driven workflow to construct patient-specific electrophysiology (EP) models of HCM by integrating multimodal clinical imaging with Bayesian model calibration. Anatomically detailed torso-heart finite-element models were generated for 17 HCM patients using thoracic computed tomography (CT), cardiac magnetic resonance imaging (CMR), and 252-electrode BSP recordings. Ventricular depolarisation and repolarisation were simulated using a reaction-eikonal (RE) formulation coupled to a biophysically detailed ToR-ORd-dynCl ionic model. Emulator-based Bayesian history matching (HM) was used to personalise EP parameters, with staged calibration of QRS and T-wave morphology informed by targeted sensitivity analysis. The calibrated cohort reproduced clinical BSP morphology with Pearson correlation coefficient (PCC) ≥0.6 for a median of 94.0% (IQR: 91.6 to 96.8%) of electrodes, achieving a median PCC of 0.89 (IQR: 0.80 to 0.94) across the full 252-electrode vest. Calibration substantially reduced uncertainty in the high-dimensional EP parameter space while yielding physiologically plausible conduction and repolarisation properties. Models calibrated exclusively to sinus rhythm robustly generalised to right-ventricular (RV) apical pacing without parameter retuning, reproducing clinically observed pacing-induced trends in depolarisation and repolarisation. Exploratory analysis revealed biologically consistent associations between inferred EP parameters and patient demographics.

Conclusions: This study demonstrates that high-density BSP data can be used to functionally personalise mechanistic EP in HCM. The framework captures intrinsic patient-specific EP properties and generalises beyond the calibration condition, supporting its use for mechanistic investigation of arrhythmogenic substrate.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

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stelong/GPErks

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f46ec3c142eeff79ce415d9438eb9c36d70813a0, 16 May 2025
Languages: Python (59), Jupyter (12)
Size: 99 files, 71 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (setup.py, requirements/requirements-dev.txt, requirements/requirements.txt), continuous integration, 12 notebooks
Not found: CITATION.cff, tests, documentation
Tools: NumPy (36 files), PyTorch (31 files), Matplotlib (19 files), pandas (7 files), scikit-learn (7 files), SciPy (7 files), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
73 files

CEMRG-publications

License: none: the authors keep all their rights
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Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)

Slicer/Slicer

License: other
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Commit: d38aed75ec317a5759114440fca842cf19ed64ea, 26 September 2026
Languages: C/C++ (1005), Python (288), Shell (10), C++ (5), JavaScript (2), C (1)
Size: 4,841 files, 1,311 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: license file, CITATION.cff, environment (.github/actions/slicer-build/Dockerfile), tests, continuous integration, documentation
Not found: README
Tools: NumPy (18 files), pydicom (9 files), SimpleITK (5 files), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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wasserth/TotalSegmentator

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 137311b50e2361fe38f45447808f9d11db259056, 23 September 2026
Languages: Python (107), Shell (6)
Size: 401 files, 113 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (Dockerfile, pyproject.toml, setup.py), tests, continuous integration
Not found: CITATION.cff, documentation
Tools: NumPy (66 files), NiBabel (57 files), SciPy (18 files), PyTorch (10 files), Pillow (9 files), scikit-image (8 files), nnU-Net (7 files), Matplotlib (6 files), pandas (5 files), XGBoost (4 files), CuPy (2 files), NetworkX (2 files), pydicom (2 files), scikit-learn (2 files), ANTs (1 file), MONAI (1 file), PyRadiomics (1 file), SimpleITK (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
115 files

aneic/meshtool

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 394e8c7555b79173cf0bf36fa716692271a5f780, 25 May 2026
Languages: C++ (78), C/C++ (37), Shell (1), Python (1)
Size: 255 files, 117 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (switches.def)
Not found: CITATION.cff, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
119 files

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Tracing map

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Data

No dataset and no data link were found in the paper.

Data Availability

We have made all patient specific meshes used in this study available to the community in .vtk and carp_txt formats on Zenodo (DOI: https://doi.org/10.5281/zenodo.18890229). The code to perform the methods outlined in this paper, including pipelines for mesh processing and model calibration, are available publicly on Github (https://github.com/CEMRG-publications). Third-party open-source tools used in this work are publicly available: 3D Slicer (https://github.com/Slicer/Slicer), TotalSegmentator (https://github.com/wasserth/TotalSegmentator), Meshtool (https://bitbucket.org/aneic/meshtool/src/master/), GPErks (https://github.com/stelong/GPErks), and NeuroKit2 (https://neuropsychology.github.io/NeuroKit/). CARPentry (https://carpentry.medunigraz.at) and Simpleware (https://www.synopsys.com/simpleware/software.html) are available upon licensing from their respective developers.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 15 MeSH terms, 8 funders, 64 references.

Cite

This paper

Malik, S., Cicci, L., Qayyum, A., Ghelani, R., Chow, J.-j., Mohal, J. S., Whinnett, Z. I., Varnava, A., Plank, G., Kanagaratnam, P., & Niederer, S. A. (2026). Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy. PLoS computational biology, 22(7), e1014555. https://doi.org/10.1371/journal.pcbi.1014555

BibTeX

@article{malik2026body,
author = {Malik, Shambhavi and Cicci, Ludovica and Qayyum, Abdul and Ghelani, Rahul and Chow, Ji-jian and Mohal, Jagdeep Singh and Whinnett, Zachary I and Varnava, Amanda and Plank, Gernot and Kanagaratnam, Prapa and Niederer, Steven A},
title = {{Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy}},
journal = {PLoS computational biology},
year = {2026},
month = jul,
volume = {22},
number = {7},
pages = {e1014555},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014555},
url = {https://doi.org/10.1371/journal.pcbi.1014555},
pmid = {42507707},
pmcid = {PMC13432148}
}

RIS

TY - JOUR
AU - Malik, Shambhavi
AU - Cicci, Ludovica
AU - Qayyum, Abdul
AU - Ghelani, Rahul
AU - Chow, Ji-jian
AU - Mohal, Jagdeep Singh
AU - Whinnett, Zachary I
AU - Varnava, Amanda
AU - Plank, Gernot
AU - Kanagaratnam, Prapa
AU - Niederer, Steven A
TI - Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/07/27
VL - 22
IS - 7
SP - e1014555
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014555
UR - https://doi.org/10.1371/journal.pcbi.1014555
LA - en
ER -

CSL-JSON

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