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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion, evaluates...
Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for diagnosing...

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Related Experiment Video

Updated: Jun 19, 2026

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting
03:40

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting

Published on: January 17, 2025

Detection of Left Ventricular Outflow Obstruction From Standard B-Mode Echocardiogram Videos Using Deep Learning.

Victoria Yuan1, Hirotaka Ieki2, Christina Binder3

  • 1David Geffen School of Medicine at University of California, Los Angeles, California, USA; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

JACC. Advances
|June 18, 2026
PubMed
Summary

An artificial intelligence model can detect left ventricular outflow tract (LVOT) obstruction using standard echocardiography videos. This AI tool aids in identifying patients with hypertrophic cardiomyopathy (HCM) who may need further evaluation for obstructive HCM.

Keywords:
artificial intelligencedeep learningechocardiographyobstructive hypertrophic cardiomyopathy

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Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

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Last Updated: Jun 19, 2026

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting
03:40

Point-of-Care Ultrasound for Peripheral Veno-Arterial Extracorporeal Membrane Oxygenation Without Left Ventricular Venting

Published on: January 17, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Hypertrophic cardiomyopathy (HCM) affects millions globally, increasing risks of sudden death and heart failure.
  • Obstructive HCM requires specific treatments, but left ventricular outflow tract (LVOT) obstruction is often underdiagnosed by echocardiography.
  • Artificial intelligence (AI) offers potential to improve the detection of LVOT obstruction.

Purpose of the Study:

  • To develop a deep learning model for detecting LVOT obstruction using non-Doppler B-mode echocardiography.
  • To assess the model's performance and generalizability across diverse patient cohorts.

Main Methods:

  • Trained a deep learning model on 2,396 patients with LVOT obstruction and 6,177 controls using apical 4-chamber B-mode echocardiographic videos.
  • Defined LVOT obstruction by gradient or systolic anterior motion of the mitral valve.
  • Validated the model on independent test sets from three major healthcare systems.

Main Results:

  • The AI model achieved strong performance in detecting LVOT obstruction, with an AUC of 0.858 at Cedars-Sinai.
  • Generalizable performance was observed across Kaiser Permanente (AUC 0.817) and Stanford (AUC 0.836) cohorts.
  • Consistent accuracy was noted across various patient subgroups, including those with hyperdynamic function or valvular disease.

Conclusions:

  • An AI model was successfully developed to detect LVOT obstruction from standard echocardiographic videos.
  • This AI tool can help identify patients who may benefit from further cardiac workup for obstructive HCM.
  • The findings suggest AI can enhance diagnostic capabilities in hypertrophic cardiomyopathy.