AIMC Topic: Heart Defects, Congenital

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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...
Saliva Cyanosis Heart Defects, Congenital ROC Curve Metabolomics Humans Metabolome Artificial Intelligence Pregnancy Female Prenatal Diagnosis Infant, Newborn Adult
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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...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Heart Heart Defects, Congenital Humans Deep Learning
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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...
Child Treatment Outcome Predictive Value of Tests Adolescent Time Factors Child, Preschool Cardiac Surgical Procedures Heart Defects, Congenital Young Adult Adult Male Risk Assessment Postoperative Complications Infant Female Infant, Newborn Risk Factors Humans Artificial Intelligence Decision Support Techniques
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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...
Machine Learning Algorithms Female Humans China ROC Curve Heart Defects, Congenital Male Risk Assessment Case-Control Studies
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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...
Neural Networks, Computer Algorithms Echocardiography Humans Image Processing, Computer-Assisted Heart Ventricles Heart Heart Defects, Congenital
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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...
Models, Cardiovascular Humans Deep Learning Heart Defects, Congenital
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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...
Algorithms Pregnancy Female Humans Echocardiography Neural Networks, Computer Ultrasonography, Prenatal Image Interpretation, Computer-Assisted Deep Learning Heart Defects, Congenital Fetal Heart
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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 Child Machine Learning Heart Defects, Congenital Biomarkers Precision Medicine
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The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method.

Medical & biological engineering & computing Aug 5, 2024
Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limited by noise during heart sound acquisition. A limitation of existing noise reduction schemes is that ...
Algorithms Signal Processing, Computer-Assisted Signal-To-Noise Ratio Heart Defects, Congenital Heart Sounds Humans Neural Networks, Computer
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Host-derived protein profiles of human neonatal meconium across gestational ages.

Nature communications Jul 17, 2024
Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted ...
Proteomics Meconium Proteome Female Humans Infant, Premature Gestational Age Infant, Newborn Machine Learning Gastrointestinal Diseases Male Pregnancy Heart Defects, Congenital
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