Automated Detection of Pediatric Pneumonia via Clinically Driven AI Analysis of Lung Ultrasound.

Lung ultrasound (LUS) is increasingly utilized for diagnosing pediatric pneumonia due to its bedside accessibility, radiation-free nature, and high diagnostic sensitivity. However, broader clinical adoption remains hindered by operator dependency, inconsistent interpretation, and training challenges, particularly among trainees and less-experienced health care providers. Currently, there is an unmet need for practical tools that help trainees reliably detect pneumonia-related ultrasound findings. In this technical innovation study, we evaluated a semi-automated, artificial intelligence (AI)-assisted system designed to identify clinically relevant lung abnormalities, including pleural line thickening, consolidation morphology, and B-line patterns. Our computerized analysis demonstrated the system's technical capability to accurately detect these structural changes with minimal user interaction. Although our primary aim was to assess diagnostic feasibility, the intuitive nature and real-time visual annotations provided by this AI tool highlight its strong potential for future integration into educational contexts. By visually assisting trainees in recognizing key sonographic features, this technology can facilitate learning, improve detection skills, and effectively support the training of health care providers performing pediatric LUS.