Congress:
ECR25
Poster Number:
C-12171
Type:
Poster: EPOS Radiologist (scientific)
DOI:
10.26044/ecr2025/C-12171
Authorblock:
C. Thouly1, B. Dufour1, N. Heracleous1, D. Moreira1, D. Ribeiro1, B. Rizk1, F. Zanca2; 1Sion/CH, 2Leuven/BE
Disclosures:
Cyril Thouly:
Nothing to disclose
Benoît Dufour:
Nothing to disclose
Natalie Heracleous:
Nothing to disclose
Daniela Moreira:
Nothing to disclose
Diana Ribeiro:
Nothing to disclose
Benoît Rizk:
Nothing to disclose
Federica Zanca:
Nothing to disclose
Keywords:
Artificial Intelligence, Bones, Paediatric, Conventional radiography, Digital radiography, RIS, Computer Applications-General, Cost-effectiveness, Structured reporting, Education and training, Outcomes
Bone age assessment is an essential tool in pediatric radiology, widely used to evaluate growth disorders and endocrine pathologies. Traditionally, radiologists determine bone age using the Greulich and Pyle atlas, a method that, while well-established, is time-consuming and subject to inter-observer variability. The introduction of artificial intelligence offers a promising alternative that can streamline radiology workflows and enhance diagnostic consistency. This study investigates whether AI-driven bone age estimation using the BoneXpert software can improve efficiency in a multi-center setting by reducing reporting time while maintaining accuracy.