Assessing the Severity of Congenital Heart Defects in Neonates through AI-based Image Segmentation Techniques

Abstract

This paper focuses on assessing the severity of congenital heart defects in neonates through AI-based image segmentation techniques. Congenital heart defects are common in newborns, affecting approximately one in every 200 babies. Although some of these defects pose a life-threatening risk, most are treatable, and successful treatment is possible with surgery. Heart defects can arise during fetal development or after birth, and they affect the heart valves, septa, or vessels, disrupting blood flow to various organs and depriving them of complete oxygen. AI-based image segmentation techniques can be used to assess the severity of these defects, providing doctors with critical information for effective treatment planning.

The study proposes segmentation of cardiac images using an AI-based neural network to assess the severity of cardiac arrest in neonates. Cardiac imaging is essential for the diagnosis and management of congenital heart defects. However, traditional image segmentation methods are time-consuming and often require manual intervention. The proposed AI-based technique can improve the accuracy and efficiency of image segmentation, providing doctors with a detailed assessment of the severity of the defect.

The study also provides insights into different types of heart defects in neonates, such as secundum, primus, and sinus venosus perforations, heart valve blockages, translocated blood vessels, and rheumatic fever. The severity of these defects can vary, and accurate diagnosis is critical for effective treatment planning. The paper also highlights the importance of prenatal testing and the role of family history, maternal health, and lifestyle choices in preventing early heart disease. Paper highlights the importance of using AI-based image segmentation techniques to assess the severity of congenital heart defects in neonates. The proposed technique can improve the accuracy and efficiency of image segmentation, providing doctors with critical information for effective treatment planning. The paper also emphasizes the importance of prenatal testing and the role of family history, maternal health, and lifestyle choices in preventing early heart disease. Ultimately, the goal is to ensure the best possible outcomes for neonates with congenital heart defects