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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Deep-learning-based real-time individualization for reduce-order haemodynamic model.

Bao Li¹, Guangfei Li¹, Jincheng Liu¹

¹Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.

Computers in Biology and Medicine

|April 18, 2024

Summary

This summary is machine-generated.

This study introduces a double-path neural network (DPNN) for accurate, real-time patient-specific modeling of human blood circulation using lumped parameter models (LPMs). This enhances the clinical applicability of haemodynamic simulations.

Keywords:

Double-path neural network Haemodynamics Individualization Lumped parameter model Real-time

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Area of Science:

Biomedical Engineering
Computational Physiology
Medical Simulation

Background:

Reduced-order lumped parameter models (LPMs) offer computational efficiency for haemodynamic simulations.
However, LPMs face limitations in achieving accurate patient-specific computations for clinical use.

Purpose of the Study:

To develop a highly accurate method for individualizing LPMs to improve their clinical applicability.
To enable real-time, patient-specific numerical simulations in haemodynamics.

Main Methods:

Collected clinical haemodynamic data from 323 individuals, expanding to 5000 synthesized cases.
Established a lumped parameter model (LPM) of the human blood circulation system.
Designed a double-path neural network (DPNN) to predict individual LPM parameters from haemodynamic indicator waveforms and features.

Main Results:

DPNN demonstrated good convergence during training.
In a test set of 100 cases, DPNN-derived LPMs showed a mean difference of approximately 10% compared to clinical measurements.
DPNN prediction for 100 cases took only 4 seconds.

Conclusions:

The proposed DPNN enables real-time and accurate individualization of LPMs.
This method provides a foundation for patient-specific haemodynamic simulations, potentially benefiting clinical applications.