Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model
- Authors
- Ahn, Kyung-Sik; Bae, Byeonguk; Jang, Woo Young; Lee, Jin Hyuck; Oh, Saelin; Kim, Baek Hyun; Lee, Si Wook; Jung, Hae Woon; Lee, Jae Won; Sung, Jinkyeong; Jung, Kyu-Hwan; Kang, Chang Ho; Lee, 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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- Appears in
Collections - 2. Clinical Science > Department of Orthopedic Surgery > 1. Journal Articles
- 2. Clinical Science > Department of Radiology > 1. Journal Articles
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