Automated detection of pediatric pneumonia via clinically driven AI analysis of lung ultrasound

Lung ultrasound (LUS) is an increasingly utilized tool for diagnosing pediatric pneumonia, offering key advantages such as radiation-free imaging, bedside accessibility, and high sensitivity. However, widespread clinical adoption remains limited due to its operator dependency. This study introduces an automated, AI-driven approach that focuses on clinically motivated features based on known pathological and sonographic findings of pneumonia. Using computerized ultrasound analysis, we assess the automated detection of structural lung abnormalities in pediatric patients. We further evaluated key quantitative metrics derived from automated segmentation, including pleural line thickness, consolidation morphology, and B-line characteristics. Our findings support the feasibility of an AI-assisted diagnostic framework that integrates clinical features, promotes standardized LUS interpretation, reduces operator variability, and enhances diagnostic accuracy in pediatric pneumonia diagnosis.