Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

PCSK9 Inhibitors in Heart Transplant Recipients.

Transplantation proceedings·2026
Same author

Sinogram-based flow estimation in computed tomography using a physics-informed neural network: Impact of gantry rotation speed, X-ray fluence and pulsed acquisition on accuracy.

Medical physics·2026
Same author

Neutrophils exhibit distinct migration phenotypes that are modulated by transendothelial migration.

Communications biology·2026
Same author

Left atrial flow and thrombosis risk from 4D CT contrast dynamics by physics-informed neural network and indicator dilution theory.

bioRxiv : the preprint server for biology·2026
Same author

DIA-PINN: A physics-informed machine learning method to estimate global intrinsic diastolic chamber properties of the left ventricle from pressure-volume data.

American journal of physiology. Heart and circulatory physiology·2026
Same author

In silico assessment of arrhythmic risk following the implantation of engineered heart tissues in porcine hearts with varying infarct locations.

PLoS computational biology·2026

Related Experiment Video

Updated: Jun 22, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

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

Published on: February 13, 2021

6.4K

Deriving phenotype-representative left ventricular flow patterns by reduced-order modeling and classification.

María Guadalupe Borja1, Pablo Martinez-Legazpi2, Cathleen Nguyen3

  • 1Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA.

Computers in Biology and Medicine
|June 30, 2024
PubMed
Summary
This summary is machine-generated.

Reduced-order models (ROMs) simplify complex cardiac flow patterns into interpretable metrics. This machine learning approach effectively differentiates between dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and healthy individuals using echocardiogram data.

Keywords:
Blood flow imagingHeart failureMachine learningPrincipal component analysisVector flow mapping

More Related Videos

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.2K
Echocardiographic Characterization of Left Ventricular Structure, Function, and Coronary Flow in Neonate Mice
07:55

Echocardiographic Characterization of Left Ventricular Structure, Function, and Coronary Flow in Neonate Mice

Published on: April 7, 2022

2.8K

Related Experiment Videos

Last Updated: Jun 22, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

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

Published on: February 13, 2021

6.4K
Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.2K
Echocardiographic Characterization of Left Ventricular Structure, Function, and Coronary Flow in Neonate Mice
07:55

Echocardiographic Characterization of Left Ventricular Structure, Function, and Coronary Flow in Neonate Mice

Published on: April 7, 2022

2.8K

Area of Science:

  • Cardiovascular Imaging
  • Biomedical Engineering
  • Computational Fluid Dynamics

Background:

  • Clinical translation of advanced cardiac flow imaging is hindered by challenges in extracting representative flow patterns and metrics.
  • Reduced-order models (ROMs) offer a promising strategy for deriving simple, interpretable intraventricular flow metrics.
  • Integrating ROMs with machine learning (ML) can enhance diagnosis and risk stratification in cardiac patients.

Purpose of the Study:

  • To investigate the utility of ROMs derived from 2D color-Doppler echocardiograms for classifying cardiac conditions.
  • To develop and validate a simple, interpretable metric for differentiating between non-ischemic dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and healthy controls.
  • To explore the potential of ML-based analysis of ROMs for clinical applications in cardiac flow assessment.

Main Methods:

  • Proper Orthogonal Decomposition (POD) was applied to 2D color-Doppler echocardiograms from 81 DCM patients, 51 HCM patients, and 77 controls to build patient- and cohort-specific ROMs.
  • Three ML classifiers were tested on ROMs, with hyperparameter optimization used to maximize classification power in supervised models.
  • Vector flow mapping was employed for visualization and interpretation of flow patterns and ML results.

Main Results:

  • POD-based ROMs effectively represented all cohorts, with the principal mode capturing over 80% of flow kinetic energy.
  • The ratio of kinetic energy between the second (vortex) and first (jet) POD modes, termed the vortex-to-jet (V2J) energy ratio, emerged as a key discriminating metric.
  • The V2J ratio achieved high accuracy in differentiating between DCM, HCM, and control groups, with areas under the ROC curve ranging from 0.81 to 0.95.

Conclusions:

  • Modal decomposition using POD can generate ROMs that capture essential cardiac flow dynamics.
  • Simple, interpretable flow metrics, such as the V2J energy ratio, can be derived from these ROMs.
  • These metrics demonstrate significant potential for discriminating between cardiac disease states and are well-suited for ML-based analysis.