DefinePK

Abstract

Neonatal congenital heart defects (CHDs) are still one of the main reasons babies get sick or die around the world. For babies who are harmed, finding the problem early and acting on it quickly are very important for better their long-term health. But standard testing methods often have problems with how sensitive they are, how accurate they are, and how early they can tell if a disease will get worse. This article looks at how deep learning (DL) technologies might be able to help with figuring out which babies are at the highest risk and getting them help as soon as possible if they are born with a heart problem. Big sets of medical images, medical records, and genetic information can help deep learning systems find small trends in the data that might not be obvious with normal analysis. In this paper, we suggest a mixed deep learning model that combines convolutional neural networks (CNNs) for image-based analysis of echocardiograms and MRI scans with recurrent neural networks (RNNs) to look for trends in long-term clinical data. The model has been taught to predict early on how bad a CHD will be and what problems might come up, like heart failure or palpitations. Also, the model is used to divide newborns into different risk groups. This lets doctors focus on the most dangerous babies and make treatment plans that fit their needs.  We test the model on a large group of newborns with different types of congenital heart defects and compare its results to both standard diagnosis methods and other machine learning models.