Artificial intelligence applications have shown success in different medical and health care
domains, and cardiac imaging is no exception. However, some machine learning models …

This narrative review delves into the potential of artificial intelligence (AI) in predicting,
stratifying risk, and personalizing treatment planning for congenital heart disease (CHD) …

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in
obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is …

Electrocardiography is essential for the early diagnosis and treatment of heart diseases, as
undiagnosed heart diseases can lead to unfortunate outcomes such as patient loss …

The realm of prenatal diagnosis has witnessed remarkable advancements in recent years,
primarily due to the widespread use of ultrasonography. This non-invasive imaging …

Congenital heart disease (CHD) is a severe health risk for newborns. Early detection of
abnormalities in fetal cardiac structure and function during pregnancy can help patients seek …

Medical advancements in the diagnosis, surgical techniques, perioperative care, and
continued care throughout childhood have transformed the outlook for individuals with …

Background Congenital heart disease (CHD) is one of the most common birth defects in the
world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely …

Congenital heart disease (CHD) is the most prevalent congenital ailment. One in every four
newborns born with serious coronary artery disease will require surgery or other early …

Deep learning models have been used in the early prediction of congenital heart defects in
pregnant women. These models exploit data from a variety of sources to create predictive …