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Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method.
Hwang, Jisun; Yoon, Hee Mang; Hwang, Jae-Yeon; Kim, Pyeong Hwa; Bak, Boram; Bae, Byeong Uk; Sung, Jinkyeong; Kim, Hwa Jung; Jung, Ah Young; Cho, Young Ah; Lee, Jin Seong.
Afiliación
  • Hwang J; Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
  • Yoon HM; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. espoirhm@gmail.com.
  • Hwang JY; Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, College of Medicine, Pusan National University, Yangsan, Korea. jyhwang79@gmail.com.
  • Kim PH; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Bak B; University of Ulsan Foundation for Industry Cooperation, Ulsan, Korea.
  • Bae BU; VUNO, Inc., Seoul, Korea.
  • Sung J; VUNO, Inc., Seoul, Korea.
  • Kim HJ; Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Jung AY; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Cho YA; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Lee JS; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Yonsei Med J ; 63(7): 683-691, 2022 Jul.
Article en En | MEDLINE | ID: mdl-35748080
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Determinación de la Edad por el Esqueleto / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies Límite: Aged / Child / Humans / Male País/Región como asunto: Asia Idioma: En Revista: Yonsei Med J Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Determinación de la Edad por el Esqueleto / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies Límite: Aged / Child / Humans / Male País/Región como asunto: Asia Idioma: En Revista: Yonsei Med J Año: 2022 Tipo del documento: Article
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