Quantitative Ultrasound Imaging and Artificial Intelligence in Neonatal Echocardiography: Methodological Advances, Reproducibility Challenges, and Computational Perspectives

Abstract

Neonatal echocardiography remains an essential imaging modality for the assessment of congenital and hemodynamic cardiovascular abnormalities in critically ill neonates. Recent advances in quantitative ultrasound biomarkers, deformation imaging, volumetric reconstruction, and artificial intelligence (AI)-assisted analysis have substantially expanded the diagnostic capabilities of neonatal cardiovascular ultrasound imaging. This narrative review critically examines current developments in quantitative echocardiographic imaging, advanced volumetric methodologies, and AI-assisted cardiovascular ultrasound analysis, with emphasis on neonatal intensive care applications. A literature search was conducted using PubMed, Google Scholar, Scopus and Embase focusing on neonatal echocardiography, spatiotemporal image correlation (STIC), speckle-tracking echocardiography, artificial intelligence, machine learning, and quantitative cardiovascular imaging. Recent studies suggest that advanced methodologies, including speckle-tracking echocardiography, STIC-based reconstruction, automated segmentation algorithms, and deep learning frameworks, may improve image standardization, automated quantification, and congenital heart disease detection. However, important challenges persist, including operator dependency, dataset heterogeneity, limited external validation, cross-platform variability, and incomplete integration into routine neonatal intensive care workflows. Most AI-assisted echocardiographic systems remain investigational and require further prospective multicenter validation before widespread clinical implementation can be achieved.