Computerized Diagnosis Methods for Pediatric Cardiology: A Review - Shantha Selvakumari, R.; Punithakumar, Kumaradevan; Selvathi, D.; Chrisilla, S.; Sri Abinaya, R.; Ray, Nilanjan (IEEE access, 2025)
Pediatric cardiac disorders include an extensive range of heart conditions in infants, children and carried over to adolescents in certain cases. These disorders may be congenital or acquired and can vary in impacting the life of the pediatric subject which requires complex surgical procedures and certain cases that do not require medical intervention. Congenital heart disease (CHD) is present during birth and acquired cardiac diseases are developed after birth predominantly due to autoimmune responses or infections. Various acquisition techniques help in visualizing the heart, identify disorders and help the physicians to plan for operative procedures. Pediatric Cardiac Screening is one of the crucial techniques to record cardiac activity which has difficulties in acquisition as children tend to move during the procedure. The data obtained from these modalities may suffer from various artifacts which makes the diagnosis difficult for the clinicians. To make the diagnosis easier and artifact free, artificial intelligence plays a vital role. Artificial Intelligence (AI)-based techniques from traditional machine learning (ML) to deep learning (DL) techniques for classification and segmentation of pediatric cardiac signals and images are systematically reviewed. DL based studies have become a choice of research in pediatric healthcare. Support Vector Machines with linear kernels are the most commonly used ML based classifiers in the reviewed papers. DL methods use Convolutional Neural Networks (CNN) as the primary classifier and U-Net architectures are preferred for segmentation studies in the reviewed papers. There are a very few surveys available to present the diagnostics related to pediatric cardiac disorders and this paper tries to bring out the challenges along with the traditional machine learning and emerging deep learning techniques implemented in classification and segmentation of the pediatric cardiac disorders.