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Updated: Jul 7, 2025

Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography

Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography

Published on: October 28, 2020

An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility

Polydoros N Kampaktsis¹, Tuan A Bohoran², Mark Lebehn¹

¹Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA.

Echocardiography (Mount Kisco, N.Y.)

|December 21, 2023

Summary

This summary is machine-generated.

A new deep learning method accurately quantifies right ventricular (RV) function using 2D echocardiography (2DE). This approach shows promise for clinical use, though larger studies are needed for validation.

Keywords:

deep learning echocardiography machine learning quantification right ventricle

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Last Updated: Jul 7, 2025

Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography

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Published on: October 28, 2020

Quantifying Intermembrane Distances with Serial Image Dilations

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Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

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Published on: October 28, 2020

Area of Science:

Cardiology
Artificial Intelligence
Medical Imaging

Background:

Accurate quantification of the right ventricle (RV) is crucial for diagnosing and managing cardiovascular diseases.
Traditional methods using 2D echocardiography (2DE) can be limited in accuracy and reproducibility.
Cardiac magnetic resonance imaging (CMR) is a reference standard but is less accessible than 2DE.

Purpose of the Study:

To evaluate the feasibility and accuracy of an attention-based deep learning (DL) method for RV quantification.
To compare DL-derived RV metrics from 2DE against CMR reference standards.
To explore the potential clinical applicability of this novel DL approach.

Main Methods:

Retrospective analysis of 50 adult patients with concurrent 2DE and CMR.
Development of an attention-based DL model utilizing a Feature Tokenizer and Transformer layers.
Input features included patient age, gender, and RV areas from standardized 2DE views; output was RV volume and ejection fraction (RVEF).

Main Results:

The DL model demonstrated high accuracy in predicting RV volumes (R² = .953) and RVEF (APE = 7.24% ± 4.55%).
The method showed good correlation with CMR reference values.
The DL model successfully identified cases of RV dilatation and dysfunction in the testing set.

Conclusions:

The attention-based DL method is feasible and shows promising accuracy for RV quantification using 2DE.
Further validation in larger, diverse patient cohorts is warranted.
Future research will aim to reduce the number of 2DE views required for clinical implementation.