Medizinische Universität Graz - Research portal

Logo MUG Resarch Portal

Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Hankel, S; Till, H; Schweintzger, G; Kraxner, C; Singer, G; Stranger, N; Till, T; Tschauner, S.
Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children.
Sci Rep. 2025; 15(1): 37006 Doi: 10.1038/s41598-025-09994-w [OPEN ACCESS]
PubMed PUBMED Central FullText FullText_MUG

 

Authors Med Uni Graz:
Hankel Saskia
Singer Georg
Stranger Nikolaus
Till Holger
Till Tristan
Tschauner Sebastian
Altmetrics:

Dimensions Citations:

Plum Analytics:

Scite (citation analytics):

Abstract:
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.
Find related publications in this database (using NLM MeSH Indexing)
Humans - administration & dosage
Artificial Intelligence - administration & dosage
Infant - administration & dosage
Ultrasonography - methods
Male - administration & dosage
Female - administration & dosage
Retrospective Studies - administration & dosage
Skull Fractures - diagnostic imaging
Cranial Sutures - diagnostic imaging
Child, Preschool - administration & dosage
Diagnosis, Differential - administration & dosage

© Med Uni GrazImprint