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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
Purpose 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...
Read more Methods and materials This study was conducted across eleven diagnostic centers in Switzerland, all utilizing the BoneXpert AI software for bone age estimation. The study was divided into two phases to compare the efficiency of manual versus AI-assisted reporting. In Phase 1 (P1), radiologists manually estimated bone age and generated reports using the Greulich and Pyle atlas. In Phase 2 (P2), BoneXpert provided automatic bone age and height predictions, which were seamlessly integrated into structured reports. Radiologists had the option to review and...
Read more Results The shift from manual to AI-assisted reporting resulted in a significant decrease in the time required to generate a report. The mean reporting time in Phase 1 was 3.5 minutes, with a range of 1 to 13 minutes, whereas in Phase 2, the mean time decreased to 1.58 minutes, with a range of 1 to 9 minutes. This represents a 55% reduction in reporting time, demonstrating the efficiency of AI-assisted reporting (p < 0.001). Figure 1 illustrates this reduction, showing...
Read more Conclusion We conducted a multi-center study using the BoneXpert AI algorithm, replacing manual estimation of bone age with an AI-driven workflow. Results show a 55% reduction in reporting time compared to the traditional Greulich and Pyle method. The findings of this study confirm that AI-assisted bone age assessment significantly enhances radiology workflow efficiency, reducing reporting time by 55% without compromising diagnostic quality. The automatic integration of AI-generated results into structured reports streamlines the reporting process, allowing radiologists to focus on more...
Read more References William Walter Greulich, Sarah Idell Pyle. Radiographic Atlas of Skeletal Development of the Hand and Wrist. (1959)Thodberg HH, Kreiborg S, Juul A, Pedersen KD: The BoneXpert Method for Automated Determination of Skeletal Maturity, IEEE Trans Medical Imaging, Vol. 28 (1), pp 52-66 (2009)
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