Detection of Prenatal Cardiac Disease using Computer Vision and Artificial Intelligence

Abstract

Prenatal cardiac anomalies, commonly referred to as congenital heart defects (CHDs), comprise a spectrum of pathologies that adversely affect cardiac function. There is a correlation between the numerous risks of cardiovascular diseases and the pressing requirement for precise, reliable, and efficient methods of early detection. The contemporary epoch of voluminous data presents a plethora of novel prospects for clinicians to utilize artificial intelligence in order to identify and enhance treatment for pediatric patients and those afflicted with congenital heart disease. Machine learning, a prevalent technique in the field of artificial intelligence, has been utilized to forecast various outcomes in obstetrics. The application of artificial intelligence in real-time electronic health recording and predictive modelling has demonstrated promising outcomes in the domain of fetal monitoring. The present research provides an in-depth review of recent advancements and challenges in the application of artificial intelligence techniques, such as deep learning and computer vision, for the detection of congenital heart disease.