Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prediction of soil shear strength using hybrid machine learning approaches for performance and interpretability analysis.

Scientific reports·2026
Same author

Anti-Ku Antibodies in a Patient With a Polymyositis-Systemic Sclerosis Overlap Syndrome in Association With Autoimmune Hepatitis.

Cureus·2026
Same author

Congenital heart disease diagnosis using machine learning: a systematic literature review.

Frontiers in medicine·2026
Same author

EEG-based harmful brain activity classification using deep learning and feature fusion.

Scientific reports·2026
Same author

Beyond spirometry: understanding COPD origins to support a new diagnostic approach.

ERJ open research·2026
Same author

Hybrid vision transformer framework for congenital heart disease diagnosis.

Scientific reports·2026

Related Experiment Video

Updated: Jun 5, 2025

Implantation of Total Artificial Heart in Congenital Heart Disease
07:27

Implantation of Total Artificial Heart in Congenital Heart Disease

Published on: July 18, 2014

24.6K

Accurately assessing congenital heart disease using artificial intelligence.

Khalil Khan1, Farhan Ullah2, Ikram Syed3

  • 1Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary

Machine learning (ML) models offer improved accuracy in predicting congenital heart disease (CHD) mortality risk. This review analyzes ML methods, datasets, and future directions for enhanced CHD diagnosis and care.

Keywords:
Artificial intelligenceCongenital heart diseaseCritical aortic stenosisEchocardiographyHypoplastic left heart syndromeML algorithmsParental ultrasound

More Related Videos

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.5K
Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

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

Published on: February 10, 2022

3.4K

Related Experiment Videos

Last Updated: Jun 5, 2025

Implantation of Total Artificial Heart in Congenital Heart Disease
07:27

Implantation of Total Artificial Heart in Congenital Heart Disease

Published on: July 18, 2014

24.6K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.5K
Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

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

Published on: February 10, 2022

3.4K

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Congenital heart disease (CHD) is a major cause of newborn mortality, especially in low-resource settings.
  • Limited healthcare resources exacerbate the impact of CHD globally.
  • Accurate risk assessment and early diagnosis are critical for improving infant outcomes.

Purpose of the Study:

  • To provide a comprehensive analysis of machine learning (ML) methods for CHD diagnosis over the past eight years.
  • To describe available datasets for CHD research and their relevance to ML applications.
  • To critically evaluate existing ML algorithms, their strengths, weaknesses, and limitations in CHD identification.

Main Methods:

  • Systematic review and analysis of ML techniques applied to CHD diagnosis.
  • Evaluation of various datasets used in CHD research, including data collection and characteristics.
  • Critical assessment of the performance and limitations of current ML algorithms for CHD.

Main Results:

  • ML models show significant potential in accurately assessing CHD mortality risk.
  • ML algorithms enhance diagnostic accuracy by identifying complex patterns often missed by clinicians.
  • A review of ML methods, datasets, and algorithms provides insights into current capabilities and challenges.

Conclusions:

  • ML offers a promising avenue for improving the identification and management of CHD.
  • Further research is needed to fully leverage ML's potential in CHD diagnosis and treatment.
  • Future directions focus on enhancing the efficacy of ML for better infant outcomes in CHD cases.