Related Experiment Video
Updated: Jun 19, 2026
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.
Insights
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.
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.
Background:
Hypertrophic cardiomyopathy (HCM) affects 20 million individuals globally, with increased risk of sudden death and heart failure. Although cardiac myosin inhibitors show great promise as disease-specific treatment, current indications are for obstructive HCM. Obstruction is not always well characterized by echocardiography. Artificial intelligence might assist in the improving the underdiagnosis of left ventricular outflow tract (LVOT) obstruction.
Objectives:
The authors aimed to develop a deep learning model to detect LVOT obstruction from non-Doppler B-mode echocardiography.
Methods:
We identified 2,396 patients with LVOT obstruction and 6,177 control patients matched by age, sex, and septal thickness. LVOT obstruction was defined as the presence of an LVOT gradient or systolic anterior motion of the mitral valve on final echocardiography report. A deep learning model was trained on non-Doppler apical 4-chamber B-mode echocardiographic videos to detect the presence of outflow obstruction identified later in the same study by spectral Doppler. We evaluated our model on held-out test sets from Cedars-Sinai Medical Center, Stanford Healthcare, and Kaiser Permanente Northern California.
Results:
In a test set of 3,848 videos from Cedars-Sinai Medical Center, our model demonstrated strong performance, detecting LVOT obstruction with area under the receiver operating characteristic curve (AUC) of 0.858 (95% CI: 0.847-0.870). The model demonstrated generalizable performance in the Kaiser Permanente Northern California cohort with AUC of 0.817 (95% CI: 0.740-0.922) and Stanford Healthcare with AUC 0.836 (95% CI: 0.807-0.827). Performance was consistent across patient subgroups, including those with hyperdynamic left ventricular function, pre-existing valvular disease, and small left ventricular cavity size.
Conclusions:
In this study, we developed an artificial intelligence model to detect LVOT obstruction from standard apical 4-chamber videos, highlighting patients who may benefit from more detailed cardiac workup for obstructive HCM.
Related Concept Videos
01:17Imaging Studies for Cardiovascular System I:Echocardiography
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion, evaluates...
01:20Imaging Studies for Cardiovascular System II:Types of Echocardiography
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...