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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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MultiEchoNet: a multi-task network for left ventricular ejection fraction and mitral annulus diameter calculation.

Mengli Zhou¹, Mingen Zhong², Kang Fan³

¹Xiamen University of Technology, Xiamen, 361024, Fujian, China.

Medical & Biological Engineering & Computing

|January 5, 2026

Summary

This summary is machine-generated.

MultiEchoNet automates left ventricular function analysis using weakly supervised learning, improving cardiovascular disease diagnosis. This deep learning model accurately quantifies ejection fraction and annulus diameter from ultrasound images, enhancing efficiency and accuracy.

Keywords:

Echocardiography Keypoint detection Multi-task learning Parameter calculation Semantic segmentation

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Echocardiographic Assessment of Cardiac Anatomy and Function in Adult Rats

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Published on: December 13, 2019

Area of Science:

Cardiovascular Imaging
Medical Artificial Intelligence
Echocardiography Analysis

Background:

Accurate quantification of left ventricular function is crucial for diagnosing cardiovascular diseases.
Current clinical methods rely on time-consuming manual segmentation of ultrasound images.
Existing automated methods may lack efficiency and accuracy in complex cardiac analyses.

Purpose of the Study:

To develop an automated system, MultiEchoNet, for quantifying left ventricular ejection fraction (LVEF) and mitral annulus diameter (MAD).
To address limitations of manual segmentation by employing a weakly supervised learning strategy.
To enhance the efficiency and accuracy of cardiovascular disease diagnosis through advanced AI.

Main Methods:

Introduced MultiEchoNet, a multi-task deep learning network utilizing weakly supervised learning.
Integrated a novel task propagation module for efficient global semantic information capture and reduced computational cost.
Employed a multi-task Transformer module for cross-task information extraction and mutual guidance, enabling concurrent segmentation and keypoint localization.
Utilized peak detection for identifying end-systolic and end-diastolic frames for precise parameter calculation.

Main Results:

Achieved high performance on public datasets (EchoNet-Dynamic, CAMUS) with Dice similarity coefficients of 93.51% and 93.18% for segmentation.
Obtained excellent keypoint similarity scores (0.958 and 0.940) and high correlation coefficients for LVEF (0.845, 0.82) and MAD (0.971, 0.963).
Demonstrated robust support for auxiliary diagnosis of cardiovascular diseases.

Conclusions:

MultiEchoNet effectively automates the quantification of left ventricular function parameters.
The proposed weakly supervised multi-task learning approach significantly improves accuracy and efficiency in echocardiographic analysis.
This AI-driven tool shows strong potential for clinical application in cardiovascular disease diagnosis.