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Updated: Oct 29, 2025

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

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

Published on: July 20, 2022

An automated method for detecting atrial fat using convolutional neural network.

Deepa Deepa¹, Yashbir Singh¹, Ming Chen Wang¹

¹Biomedical Engineering, Chung Yuan Christian University, Zhongli, Taoyuan city.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine

|July 6, 2021

Summary

This summary is machine-generated.

This study introduces an automated Convolutional Neural Network (CNN) method for detecting atrial epicardial fat, a key factor in Atrial Fibrillation (A-fib). The CNN achieved high accuracy, offering a reliable tool for A-fib risk assessment.

Keywords:

Atrial fibrillation CT images atrial epicardial fat convolutional neural network pixel value masking

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Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Atrial Fibrillation (A-fib) is a prevalent cardiac arrhythmia linked to serious complications like stroke and heart failure.
Accumulation of epicardial fat in the atria is increasingly recognized as a significant contributor to the development of A-fib.

Purpose of the Study:

To develop and validate a fully automated Convolutional Neural Network (CNN) for the detection of atrial epicardial fat from Cardiac Computed Tomography (CT) images.
To assess the efficacy of the proposed deep learning approach in accurately identifying and quantifying atrial fat deposits.

Main Methods:

Cardiac CT images from ten patients were pre-processed to isolate the heart region.
Automated pixel value masking was employed to identify and locate epicardial fat.
A 3D heart model was constructed for visualization, and a fast, automated CNN was applied for feature selection and fat detection.

Main Results:

The CNN model achieved high performance metrics: 89.22% accuracy, 90.18% sensitivity, and 88.52% specificity in detecting atrial epicardial fat.
The automated method demonstrated robustness and reliability in identifying fat tissue.

Conclusions:

The developed CNN-based approach provides an effective and automated method for detecting atrial epicardial fat.
This deep learning technique offers a rapid, reliable, and potentially unutilized tool for aiding in A-fib risk assessment and management.