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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Updated: Jun 30, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

Shreyas Bhave¹, Victor Rodriguez¹, Timothy Poterucha²

¹Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA.

European Heart Journal

|March 19, 2024

Summary

This summary is machine-generated.

Deep learning models can accurately detect cardiac abnormalities like left ventricular hypertrophy and dilation from chest X-rays, aiding early heart failure identification. This research provides a valuable dataset for future innovation.

Keywords:

Chest X-rays Deep learning Dilated left ventricle Early detection Heart failure Left ventricular hypertrophy

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Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Early identification of cardiac structural abnormalities is crucial for managing heart failure.
Chest X-rays (CXRs) are common diagnostic tools offering potential for scalable screening.
Deep learning (DL) can analyze CXRs for signs of Stage B heart failure or worse.

Purpose of the Study:

Develop a DL model to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs.
Evaluate the model's performance in detecting these cardiac structural abnormalities.

Main Methods:

Utilized 71,589 CXRs from 24,689 patients, linked to echocardiogram data.
Extracted labels for SLVH, DLV, and a composite outcome from echocardiograms.
Trained and validated a DL model, assessing performance via AUROC and external validation.

Main Results:

The DL model achieved an AUROC of 0.79 for SLVH, 0.80 for DLV, and 0.80 for the composite outcome.
Performance was consistent on an external dataset.
The model surpassed individual radiologists and demonstrated higher sensitivity than the radiologist consensus.

Conclusions:

DL analysis of CXRs is effective for detecting LV hypertrophy and dilation.
This approach shows promise for early identification of patients with these cardiac conditions.
A public dataset of 71,589 CXRs with echocardiographic labels is now available to foster research.