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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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Efficient Ventricular Parameter Estimation Using AI-Surrogate Models.

Gonzalo D Maso Talou¹, Thiranja P Babarenda Gamage¹, Martyn P Nash^1,2

¹Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.

Frontiers in Physiology

|November 1, 2021

Summary

This summary is machine-generated.

This study uses artificial intelligence (AI) surrogate models to identify heart tissue properties and pressure. The AI approach offers a fast and robust method for diagnosing heart conditions and can be applied to other anatomical structures.

Keywords:

MLP cardiac mechanics optimisation parameter estimation surrogate model

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Area of Science:

Biomedical Engineering
Computational Biology
Cardiovascular Research

Background:

Pathological heart conditions like cardiomyopathy and heart failure alter myocardial tissue mechanics.
Understanding these mechanical changes is crucial for diagnosing and monitoring heart disease.
Previous work established AI surrogate models for simulating passive cardiac mechanics.

Purpose of the Study:

To apply AI surrogate models to identify myocardial mechanical properties and intra-ventricular pressure using inverse problem-solving.
To introduce two novel AI-based approaches for biophysical parameter identification in cardiac mechanics.
To assess the robustness and speed of these AI techniques for clinical translation.

Main Methods:

Development and application of two novel AI-based approaches to solve an inverse problem.
Utilizing AI surrogate models for simulating passive cardiac mechanics.
Analysis of ventricular data under multiple loading conditions to identify mechanical properties and pressure.

Main Results:

Both AI approaches demonstrated robustness against Gaussian noise when combining data from multiple loading conditions.
Estimation of one and two mechanical parameters was achieved in under 9 and 18 seconds, respectively.
The AI techniques provide rapid and accurate identification of cardiac mechanical properties.

Conclusions:

The proposed AI-based inverse problem-solving technique is a viable option for clinical translation of cardiac mechanics simulations.
This method can significantly improve the diagnosis and treatment of heart pathologies.
The AI estimation techniques are general and adaptable to other anatomical structures and applications.