Advancements in echocardiography for congenital heart defects (CHDs): a literature review.
Authors
Affiliations (8)
Affiliations (8)
- Department of Informatics, Mayo Clinic, Arizona, USA.
- Master of Public Health (MPH), University of Warwick, Warwick Medical School, Coventry, UK.
- Department of Pediatrics, University of Nebraska Medical Center, Omaha, Nebraska, USA.
- Department of Pediatrics, Dow Medical College, Karachi, Pakistan.
- Department of Pediatrics, Continental Medical College, Lahore Pakistan.
- Department of Pediatrics, Metro Health Pediatric Residency Program, Cleveland Ohio, USA.
- Department of Pediatrics, Liaquat National Hospital and Medical College, Karachi, Pakistan.
- Department of Critical Care Medicine, Hospital for Advanced Medicine and Surgery, Kathmandu, Nepal.
Abstract
Congenital heart defects (CHDs) are the most prevalent congenital condition globally, affecting approximately 1% of live births and contributing to significant morbidity and mortality. While advancements in diagnostic imaging have improved early detection and management, CHDs remain a leading cause of infant mortality, particularly in low- and middle-income regions. Echocardiography has evolved as a cornerstone in CHD diagnosis, with traditional modalities such as 2D echocardiography, M-mode, and Doppler imaging providing essential structural and hemodynamic assessments. However, these conventional approaches have limitations in visualizing complex congenital anomalies. The emergence of advanced echocardiographic techniques, including three-dimensional echocardiography, fetal echocardiography, speckle tracking echocardiography (STE), and stress echocardiography (SE), has significantly enhanced diagnostic accuracy, preoperative planning, and long-term monitoring. Furthermore, recent innovations in artificial intelligence (AI), deep learning, digital signal processing, and contrast-enhanced echocardiography are revolutionizing the field by automating image analysis, improving image quality, and enabling predictive modeling for CHD prognosis. This review critically compares traditional and advanced echocardiographic modalities, evaluating their respective strengths, limitations, and impact on early CHD detection. Additionally, it explores emerging AI-driven technologies and their potential to optimize echocardiographic workflows, reduce diagnostic variability, and enhance patient outcomes. By synthesizing current evidence, this review aims to provide clinicians, researchers, and healthcare policymakers with insights into the evolving role of echocardiography in CHD diagnosis and management.