AIMC Topic: Heart Defects, Congenital

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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 ...
Signal Processing, Computer-Assisted Signal-To-Noise Ratio Heart Defects, Congenital Heart Sounds Humans Neural Networks, Computer Algorithms
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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 ...
Humans Infant, Newborn Infant, Premature Machine Learning Male Pregnancy Proteomics Gestational Age Female Meconium Proteome Gastrointestinal Diseases Heart Defects, Congenital
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Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Jul 14, 2024
BACKGROUND: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster an...
Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Biomechanical Phenomena Heart Transplantation Male Fontan Procedure Heart Defects, Congenital Cluster Analysis Ventricular Function, Left Humans Female Adolescent Unsupervised Machine Learning Risk Assessment Phenotype Risk Factors Time Factors Magnetic Resonance Imaging, Cine Retrospective Studies Young Adult Predictive Value of Tests Child Adult Myocardial Contraction Treatment Outcome
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A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

IEEE journal of biomedical and health informatics Jul 2, 2024
Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to ...
Heart Defects, Congenital Aorta Pulmonary Artery Fetal Heart Female Ultrasonography, Prenatal Deep Learning Image Interpretation, Computer-Assisted Humans Algorithms Vena Cava, Superior Pregnancy
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Machine Learning-Based Critical Congenital Heart Disease Screening Using Dual-Site Pulse Oximetry Measurements.

Journal of the American Heart Association Jun 15, 2024
BACKGROUND: Oxygen saturation (Spo) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarct...
Algorithms Prospective Studies Female Infant, Newborn Humans Machine Learning Neonatal Screening Heart Defects, Congenital Oximetry Male ROC Curve Predictive Value of Tests Oxygen Saturation
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Investigation on ultrasound images for detection of fetal congenital heart defects.

Biomedical physics & engineering express May 31, 2024
Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The d...
Female Echocardiography Ultrasonography, Prenatal Deep Learning Image Processing, Computer-Assisted Humans Fetus Neural Networks, Computer Heart Defects, Congenital Algorithms Fetal Heart Pregnancy
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Heart and great vessels segmentation in congenital heart disease via CNN and conditioned energy function postprocessing.

International journal of computer assisted radiology and surgery May 30, 2024
PURPOSE: The segmentation of the heart and great vessels in CT images of congenital heart disease (CHD) is critical for the clinical assessment of cardiac anomalies and the diagnosis of CHD. However, the diverse types and abnormalities inherent in CH...
Heart Defects, Congenital Aorta Pulmonary Artery Neural Networks, Computer Humans Tomography, X-Ray Computed Imaging, Three-Dimensional Heart
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Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.

Journal of medical systems May 23, 2024
Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital hear...
Humans Artificial Intelligence Heart Defects, Congenital
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Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.

Pediatric cardiology May 10, 2024
Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. ...
Magnetic Resonance Imaging, Cine Pulmonary Valve Insufficiency Child ROC Curve Heart Defects, Congenital Female Young Adult Adult Male Echocardiography Infant Humans Adolescent Machine Learning Child, Preschool Algorithms Retrospective Studies
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Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study.

Pediatric cardiology May 9, 2024
Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train M...
Prenatal Diagnosis Prognosis Pregnancy Female Infant, Newborn Ultrasonography, Prenatal Pilot Projects Fetal Diseases Heart Defects, Congenital Humans Machine Learning
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