AI-driven innovation in pediatric ultrasound imaging and analysis

Announcement of new article publication BIO integration journal. Emerging applications of artificial intelligence (AI) in pediatric ultrasound show great potential to improve diagnostic accuracy and efficiency, especially in addressing the challenges of traditional ultrasound, such as operator dependence, inconsistent image quality, and limited quantitative analysis capabilities.

These limitations arise from the complexities inherent in interpreting pediatric ultrasound images, such as organ immaturity, movement artifacts, and intestinal gas interference. AI enhances structural recognition and provides automated and standardized measurements. AI applications can also help non-specialist doctors improve diagnostic accuracy.

This review summarizes recent advances in AI applications for pediatric ultrasound across a variety of systems, including prediagnosis, screening, detailed analysis, and decision support, as well as details technical advances, unmet challenges, and future directions. Future research can focus on intelligent cross-system feature analysis frameworks, translational applications of AI-driven pediatric ultrasound in multi-disease diagnosis, and fine-tuned models for personalized treatment based on large-scale randomized controlled trials.

This review provides up-to-date reference information for clinicians, sonographers, researchers, and biomedical engineers.