AIMC Topic: Heart Defects, Congenital

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Artificial Intelligence Iterative Reconstruction for Dose Reduction in Pediatric Chest CT: A Clinical Assessment via Below 3 Years Patients With Congenital Heart Disease.

Journal of thoracic imaging
PURPOSE: To assess the performance of a newly introduced deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in reducing the dose of pediatric chest CT by using the image data of below 3-y...

A Machine Learning-Based Clustering Analysis to Explore Bisphenol A and Phthalate Exposure from Medical Devices in Infants with Congenital Heart Defects.

Environmental health perspectives
BACKGROUND: Plastic-containing medical devices are commonly used in critical care units and other patient care settings. Patients are often exposed to xenobiotic agents that are leached out from plastic-containing medical devices, including bisphenol...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BMC pediatrics
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...

Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.

Scientific reports
This study evaluated the feasibility of HeartAssist, a novel automated tool designed for classification of fetal cardiac views, annotation of cardiac structures, and measurement of cardiac parameters. Unlike previous AI tools that primarily focused o...

The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review.

Medicina (Kaunas, Lithuania)
Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an a...

Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.

Medical image analysis
Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal ...

Double-mix pseudo-label framework: enhancing semi-supervised segmentation on category-imbalanced CT volumes.

International journal of computer assisted radiology and surgery
PURPOSE: Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited ...

Video Clip Extraction From Fetal Ultrasound Scans Using Artificial Intelligence to Allow Remote Second Expert Review for Congenital Heart Disease.

Prenatal diagnosis
OBJECTIVE: To use artificial intelligence (AI) to automatically extract video clips of the fetal heart from a stream of ultrasound video, and to assess the performance of these when used for remote second review.

A comprehensive scoping review on machine learning-based fetal echocardiography analysis.

Computers in biology and medicine
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...