Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method
- Abstract
- Purpose: To evaluate the applicability of Greulich-Pyle (GP) standards to bone age (BA) assessment in healthy Korean children using manual and deep learning-based methods.
Materials and methods: We collected 485 hand radiographs of healthy children aged 2-17 years (262 boys) between 2008 and 2017. Based on GP method, BA was assessed manually by two radiologists and automatically by two deep learning-based BA assessment (DLBAA), which estimated GP-assigned (original model) and optimal (modified model) BAs. Estimated BA was compared to chronological age (CA) using intraclass correlation (ICC), Bland-Altman analysis, linear regression, mean absolute error, and root mean square error. The proportion of children showing a difference >12 months between the estimated BA and CA was calculated.
Results: CA and all estimated BA showed excellent agreement (ICC ≥0.978, p<0.001) and significant positive linear correlations (R²≥0.935, p<0.001). The estimated BA of all methods showed systematic bias and tended to be lower than CA in younger patients, and higher than CA in older patients (regression slopes ≤-0.11, p<0.001). The mean absolute error of radiologist 1, radiologist 2, original, and modified DLBAA models were 13.09, 13.12, 11.52, and 11.31 months, respectively. The difference between estimated BA and CA was >12 months in 44.3%, 44.5%, 39.2%, and 36.1% for radiologist 1, radiologist 2, original, and modified DLBAA models, respectively.
Conclusion: Contemporary healthy Korean children showed different rates of skeletal development than GP standard-BA, and systemic bias should be considered when determining children's skeletal maturation.
- Author(s)
- Jisun Hwang; Hee Mang Yoon; Jae-Yeon Hwang; Pyeong Hwa Kim; Boram Bak; Byeong Uk Bae; Jinkyeong Sung; Hwa Jung Kim; Ah Young Jung; Young Ah Cho; Jin Seong Lee
- Issued Date
- 2022
- Type
- Article
- Keyword
- Age determination by skeleton; child; deep learning; hand bones; radiography
- DOI
- 10.3349/ymj.2022.63.7.683
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/13964
- Publisher
- YONSEI MEDICAL JOURNAL
- Language
- 영어
- ISSN
- 0513-5796
- Citation Volume
- 63
- Citation Number
- 7
- Citation Start Page
- 683
- Citation End Page
- 691
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Appears in Collections:
- Medicine > Nursing
- 공개 및 라이선스
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