Christian Booz, Ibrahim Yel, Julian Wichmann, Sabine Böttger, Ahmed Al Kamali, Moritz Hans Ernst Albrecht, Simon S. Martin, Lukas Fabian Lenga, Nicole A. Huizinga, Tommaso D’Angelo, Marco Cavallaro, Thomas J. Vogl, Boris Bodelle
- 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.
MetadatenAuthor: | 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 |
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URN: | urn:nbn:de:hebis:30:3-531214 |
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DOI: | https://doi.org/10.1186/s41747-019-0139-9 |
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ISSN: | 2509-9280 |
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Pubmed Id: | https://pubmed.ncbi.nlm.nih.gov/31993795 |
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Parent Title (English): | European radiology experimental |
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Publisher: | Springer International Publishing |
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Place of publication: | [Cham] |
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Document Type: | Article |
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Language: | English |
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Year of Completion: | 2020 |
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Date of first Publication: | 2020/01/28 |
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Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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Release Date: | 2020/03/16 |
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Tag: | Age determination by skeleton; Algorithms; Artificial intelligence; Image processing (computer-assisted); Retrospective studies |
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Volume: | 4 |
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Issue: | 1, Art. 6 |
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Page Number: | 8 |
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First Page: | 1 |
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Last Page: | 8 |
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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. |
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HeBIS-PPN: | 46470216X |
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Institutes: | Medizin / Medizin |
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Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
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Sammlungen: | Universitätspublikationen |
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Licence (German): | Creative Commons - Namensnennung 4.0 |
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