AIMC Topic: Heart Defects, Congenital

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Navigating the future of pediatric cardiovascular surgery: Insights and innovation powered by Chat Generative Pre-Trained Transformer (ChatGPT).

The Journal of thoracic and cardiovascular surgery Feb 1, 2025
INTRODUCTION: Interdisciplinary consultations are essential to decision-making for patients with congenital heart disease. The integration of artificial intelligence (AI) and natural language processing into medical practice is rapidly accelerating, ...
Clinical Decision-Making Female Male Humans Artificial Intelligence Retrospective Studies Generative Artificial Intelligence Natural Language Processing Cardiac Surgical Procedures Heart Defects, Congenital Infant Child, Preschool Child
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A comprehensive scoping review on machine learning-based fetal echocardiography analysis.

Computers in biology and medicine Jan 15, 2025
Fetal echocardiography (ultrasound of the fetal heart) plays a vital role in identifying heart defects, allowing clinicians to establish prenatal and postnatal management plans. Machine learning-based methods are emerging to support the automation of...
Ultrasonography, Prenatal Heart Defects, Congenital Fetal Heart Pregnancy Female Echocardiography Humans Neural Networks, Computer Machine Learning
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Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence.

Scientific reports Jan 15, 2025
Prenatal sonographic diagnosis of congenital heart disease (CHD) can lead to improved morbidity and mortality. However, the diagnostic accuracy of ultrasound, the sole prenatal screening tool, remains limited. Failed prenatal or early newborn detecti...
Female Infant, Newborn Adult ROC Curve Humans Artificial Intelligence Pregnancy Cyanosis Heart Defects, Congenital Prenatal Diagnosis Metabolomics Metabolome Saliva
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Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.

Medical & biological engineering & computing Jan 4, 2025
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employ...
Humans Deep Learning Image Processing, Computer-Assisted Imaging, Three-Dimensional Heart Heart Defects, Congenital
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Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.

BMC cardiovascular disorders Dec 20, 2024
INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools...
Decision Support Techniques Female Infant, Newborn Risk Factors Young Adult Adult Humans Artificial Intelligence Child, Preschool Child Heart Defects, Congenital Infant Male Treatment Outcome Predictive Value of Tests Risk Assessment Adolescent Cardiac Surgical Procedures Postoperative Complications Time Factors
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Development of machine learning-based models to predict congenital heart disease: A matched case-control study.

International journal of medical informatics Dec 2, 2024
BACKGROUND: The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of personalized CHD management strategies. We developed a machine learning-based risk stratification mod...
China ROC Curve Heart Defects, Congenital Male Risk Assessment Case-Control Studies Machine Learning Humans Algorithms Female
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Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture.

Biomedical physics & engineering express Sep 13, 2024
Echocardiography is one the most commonly used imaging modalities for the diagnosis of congenital heart disease. Echocardiographic image analysis is crucial to obtaining accurate cardiac anatomy information. Semantic segmentation models can be used t...
Heart Defects, Congenital Image Processing, Computer-Assisted Heart Heart Ventricles Humans Neural Networks, Computer Algorithms Echocardiography
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SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.

Medical image analysis Aug 8, 2024
Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve di...
Deep Learning Humans Heart Defects, Congenital Models, Cardiovascular
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A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.

IEEE/ACM transactions on computational biology and bioinformatics Aug 8, 2024
Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth paramete...
Ultrasonography, Prenatal Pregnancy Heart Defects, Congenital Humans Deep Learning Female Neural Networks, Computer Fetal Heart Echocardiography Image Interpretation, Computer-Assisted Algorithms
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Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease.

The Canadian journal of cardiology Aug 7, 2024
Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most ...
Humans Machine Learning Biomarkers Precision Medicine Child Heart Defects, Congenital
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