From Images to Interventions: AI-Driven Echocardiographic Decision-Making in Pediatric Critical Heart Diseases

Abstract

Background: Echocardiography is the cornerstone of cardiac evaluation in pediatric critical care, yet its accuracy depends heavily on operator expertise. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cardiovascular imaging, capable of automating image acquisition, segmentation, and interpretation. Despite rapid progress in adult cardiology, their integration into pediatric echocardiography remains underexplored.

Objective: To systematically synthesize evidence on the application of AI in echocardiographic decision-making for pediatric patients with congenital or critical heart disease, emphasizing diagnostic accuracy, workflow efficiency, and clinical translation.

Methods: This review followed PRISMA 2020 guidelines (PROSPERO: CRD420251165361). Comprehensive searches were conducted across PubMed, Embase, IEEE Xplore, Web of Science, Scopus, and Cochrane databases (2010–October 2025). Eligible studies applied AI/ML algorithms to pediatric echocardiographic image acquisition, lesion detection, or functional quantification. Data were extracted on model type, task, performance metrics, and clinical integration. Quality assessment used QUADAS-2 and PROBAST tools.

Results: Twenty-six studies met inclusion criteria. AI demonstrated expert-level performance in view classification, ejection fraction estimation, and congenital lesion detection, reducing interobserver variability and analysis time. Integrative pipelines and real-time guidance improved acquisition consistency and enabled bedside deployment. However, most studies were single-center with limited external validation.

Conclusion: AI-driven echocardiography enhances diagnostic precision and workflow efficiency in pediatric critical cardiology but requires multicenter validation, ethical governance, and interoperability frameworks for clinical adoption.