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Cited 6 time in webofscience Cited 6 time in scopus
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Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model

Authors
Ahn, Kyung-SikBae, ByeongukJang, Woo YoungLee, Jin HyuckOh, SaelinKim, Baek HyunLee, Si WookJung, Hae WoonLee, Jae WonSung, JinkyeongJung, Kyu-HwanKang, Chang HoLee, Soon Hyuck
Issue Date
Dec-2021
Publisher
Springer Verlag
Keywords
Puberty; Elbow; Artificial intelligence
Citation
European Radiology, v.31, no.12, pp 8947 - 8955
Pages
9
Indexed
SCIE
SCOPUS
Journal Title
European Radiology
Volume
31
Number
12
Start Page
8947
End Page
8955
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/53766
DOI
10.1007/s00330-021-08096-1
ISSN
0938-7994
1432-1084
Abstract
Objectives Bone age is considered an indicator for the diagnosis of precocious or delayed puberty and a predictor of adult height. We aimed to evaluate the performance of a deep neural network model in assessing rapidly advancing bone age during puberty using elbow radiographs. Methods In all, 4437 anteroposterior and lateral pairs of elbow radiographs were obtained from pubertal individuals from two institutions to implement and validate a deep neural network model. The reference standard bone age was established by five trained researchers using the Sauvegrain method, a scoring system based on the shapes of the lateral condyle, trochlea, olecranon apophysis, and proximal radial epiphysis. A test set (n = 141) was obtained from an external institution. The differences between the assessment of the model and that of reviewers were compared. Results The mean absolute difference (MAD) in bone age estimation between the model and reviewers was 0.15 years on internal validation. In the test set, the MAD between the model and the five experts ranged from 0.19 to 0.30 years. Compared with the reference standard, the MAD was 0.22 years. Interobserver agreement was excellent among reviewers (ICC: 0.99) and between the model and the reviewers (ICC: 0.98). In the subpart analysis, the olecranon apophysis exhibited the highest accuracy (74.5%), followed by the trochlea (73.7%), lateral condyle (73.7%), and radial epiphysis (63.1%). Conclusions Assessment of rapidly advancing bone age during puberty on elbow radiographs using our deep neural network model was similar to that of experts.
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2. Clinical Science > Department of Orthopedic Surgery > 1. Journal Articles
2. Clinical Science > Department of Radiology > 1. Journal Articles

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Kang, Chang Ho
Anam Hospital (Department of Radiology, Anam Hospital)
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