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Imaging Studies for Cardiovascular System I:Echocardiography

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion, evaluates...

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Related Experiment Video

Updated: Jun 18, 2026

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect

Siti Nurmaini¹, Ria Nova², Ade Iriani Sapitri¹

¹Intelligent System Research Group, Universitas Sriwijaya, Palembang 30139, Indonesia.

Journal of Imaging

|November 26, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning framework using Yolov8l for real-time diagnosis of cardiac septal defects (CSDs) in pediatric echocardiography. The model achieves high accuracy, improving efficiency and patient outcomes.

Keywords:

Yolo cardiac defect end-to-end pediatric

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In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

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Last Updated: Jun 18, 2026

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

In Silico Clinical Trials for Cardiovascular Disease

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Echocardiography is the standard for diagnosing cardiac septal defects (CSDs), but expert analysis is time-consuming.
Digitization and deep learning (DL) offer potential to enhance diagnostic efficiency.

Purpose of the Study:

To develop and evaluate a real-time, end-to-end deep learning framework for pediatric echocardiography video analysis.
To improve the accuracy and efficiency of cardiac septal defect (CSD) diagnosis.

Main Methods:

An advanced real-time architecture based on You Only Look Once (Yolo) techniques, specifically Yolov8l, was employed.
The framework was trained and tested on pediatric ultrasound (US) videos for CSD decision-making.

Main Results:

The Yolov8l model achieved a mean average precision (mAP) exceeding 89% in experiments.
In testing with 222 US videos, the model showed 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity.
Real-time testing on 53 videos demonstrated 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity.

Conclusions:

The proposed deep learning framework demonstrates high accuracy and effectiveness for real-time CSD diagnosis in pediatric echocardiography.
This approach shows promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology.