Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Glenohumeral allograft capsular reconstruction for recalcitrant instability of reverse total shoulder arthroplasty: a novel technique.

JSES reviews, reports, and techniques·2026

Same author

Timing of microaxial flow pump in acute myocardial infarction related cardiogenic shock: A national analysis of mortality and complications.

Cardiovascular revascularization medicine : including molecular interventions·2025

Same author

Investigating discrepancies in perceptions regarding the provision of hospital rest and relaxation spaces in Scotland during the COVID-19 pandemic and beyond: a qualitative study.

BMJ open·2025

Same author

Influenza epidemiology and co-infections within New South Wales-based multicentre health districts between 2018 and 2023.

Pathology·2025

Same author

Identifying Long COVID Patients Using General Practice Data: Challenges, Classification and Long COVID Patterns.

Studies in health technology and informatics·2025

Same author

Landscape analysis of invasive non-typhoidal salmonella (iNTS) disease and iNTS vaccine use case and demand: Report of a WHO expert consultation.

Vaccine·2025

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Utilising Surrogate Models to Approximate Cardiac Potentials when Solving Inverse Problems via Bayesian Techniques.

Abbish Kamalakkannan¹, Peter Johnston¹, Barbara Johnston¹

¹Griffith University, Brisbane, Australia.

Computing in Cardiology

|November 30, 2023

Summary

This summary is machine-generated.

This study introduces a new method for inferring cardiac bidomain parameters using surrogate models and Bayesian inference. A seventh-order model accurately estimated extracellular conductivities and fibre rotation, but not intracellular conductivities.

More Related Videos

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Related Experiment Videos

Last Updated: Jul 9, 2025

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Area of Science:

Computational biology
Biophysics
Medical imaging

Background:

Solving inverse problems in cardiac electrophysiology is computationally intensive.
Bayesian techniques often require numerous forward solutions, increasing computational cost.
Accurate inference of bidomain parameters is crucial for understanding cardiac function.

Purpose of the Study:

To develop a novel inference protocol for cardiac bidomain parameters.
To utilize surrogate modeling with Bayesian inference for efficient parameter estimation.
To assess the accuracy of different surrogate model orders for inferring conductivities and fibre rotation.

Main Methods:

Developed a surrogate model using generalized polynomial chaos techniques to approximate cardiac potentials.
Employed Bayesian inference techniques in conjunction with the surrogate model.
Investigated surrogate model orders of three and seven for parameter inference.

Main Results:

A third-order surrogate model adequately characterized extracellular conductivities and fibre rotation influence.
A seventh-order surrogate model was necessary for adequately characterizing intracellular conductivity influence.
The seventh-order model successfully inferred extracellular conductivities and fibre rotation from noisy synthetic data.

Conclusions:

The developed protocol offers a computationally efficient approach to infer bidomain parameters.
Higher-order surrogate models are essential for accurately inferring intracellular conductivities.
Inference of intracellular conductivities remains challenging under the tested scenario.