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 assessme...

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Bibliographic Details
Published in:European radiology experimental 2020-01, Vol.4 (1), p.6-6, Article 6
Main Authors: Booz, Christian, Yel, Ibrahim, Wichmann, Julian L., Boettger, Sabine, Al Kamali, Ahmed, Albrecht, Moritz H., Martin, Simon S., Lenga, Lukas, Huizinga, Nicole A., D’Angelo, Tommaso, Cavallaro, Marco, Vogl, Thomas J., Bodelle, Boris
Format: Article
Language:English
Subjects:
Accuracy
Adolescent
Age determination by skeleton
Age Determination by Skeleton - methods
Algorithms
Artificial Intelligence
Automation
Child
Child, Preschool
Diagnostic Radiology
Efficiency
Germany
Hand - diagnostic imaging
Humans
Image processing (computer-assisted)
Imaging
Internal Medicine
Interventional Radiology
Medicine
Medicine & Public Health
Neuroradiology
Original
Original Article
Radiography
Radiology
Retrospective Studies
Ultrasound
Wrist - diagnostic imaging
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Summary: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  
ISSN:2509-9280
2509-9280
DOI:10.1186/s41747-019-0139-9