Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

  • Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method. Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and gender-specific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method. Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system. Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.

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Author:Christian BoozORCiDGND, Ibrahim YelORCiDGND, Julian WichmannORCiDGND, Sabine Böttger, Ahmed Al Kamali, Moritz Hans Ernst AlbrechtORCiDGND, Simon S. MartinORCiDGND, Lukas Fabian LengaORCiDGND, Nicole A. Huizinga, Tommaso D’AngeloORCiD, Marco Cavallaro, Thomas J. VoglORCiDGND, Boris BodelleORCiDGND
URN:urn:nbn:de:hebis:30:3-531214
DOI:https://doi.org/10.1186/s41747-019-0139-9
ISSN:2509-9280
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/31993795
Parent Title (English):European radiology experimental
Publisher:Springer International Publishing
Place of publication:[Cham]
Document Type:Article
Language:English
Year of Completion:2020
Date of first Publication:2020/01/28
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/03/16
Tag:Age determination by skeleton; Algorithms; Artificial intelligence; Image processing (computer-assisted); Retrospective studies
Volume:4
Issue:1, Art. 6
Page Number:8
First Page:1
Last Page:8
Note:
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
HeBIS-PPN:46470216X
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0