Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children

Accurate differentiation between skull fractures and sutures is challenging in young children. Traditional diagnostic modalities like computed tomography involve ionizing radiation, while sonography is safer but demands expertise. This study explores the application of artificial intelligence (AI) to improve diagnostic accuracy in this context. A retrospective study utilized sonographic images of 86 children (mean age: 8.5 months) presenting with suspected skull fractures was performed. The AI approach included binary classification and object localization, with tenfold cross-validation applied to 385 images. The study compared AI performance against nine raters with varying expertise, with and without AI assistance. EfficientNet demonstrated superior classification metrics, with the B6 variant achieving the highest F1 score (0.841) and PR AUC (0.913). YOLOv11 models underperformed compared to EfficientNet in detecting fractures and sutures. Raters significantly benefited from AI-assisted diagnostics, with F1 scores improving from 0.749 (unassisted) to 0.833 (assisted). AI models consistently outperformed unassisted human raters. This study presents the first AI model differentiating skull fractures from sutures on pediatric sonographic images, highlighting AI's potential to enhance diagnostic accuracy. Future efforts should focus on expanding datasets, validating AI models on independent cohorts, and exploring dynamic sonographic data to improve the diagnostic impact.