계명대학교 의학도서관 Repository

Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model

Metadata Downloads
Affiliated Author(s)
이시욱
Alternative Author(s)
Lee, Si Wook
Journal Title
Eur Radiol
ISSN
1432-1084
Issued Date
2021
Keyword
PubertyElbowArtificial intelligence
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.
Department
Dept. of Orthopedic Surgery (정형외과학)
Publisher
School of Medicine (의과대학)
Citation
Kyung-Sik Ahn et al. (2021). Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model. Eur Radiol, 31(12), 8947–8955. doi: 10.1007/s00330-021-08096-1
Type
Article
ISSN
1432-1084
DOI
10.1007/s00330-021-08096-1
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43794
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Orthopedic Surgery (정형외과학)
Authorize & License
  • Authorize공개
  • EmbargoForever
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.