ArticleYonsei medical journal2022
Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method.
Article in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Automated skeletal maturity staging from handwrist radiographs using deep learning models: A decision support system based on Fishman's indicators.Korean journal of orthodontics · 2026Article
- Deep Learning-Based Bone Age Assessment for Predicting Final Adult Height in Girls With Central Precocious Puberty.Korean journal of radiology · 2026Article
- The Impact of Artificial Intelligence on Radiologists' Reading Time in Bone Age Radiograph Assessment: A Preliminary Retrospective Observational Study.Journal of imaging informatics in medicine · 2025Observational
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Potential of Artificial Intelligence for Bone Age Assessment in Iranian Children and Adolescents: An Exploratory Study.Archives of Iranian medicine · 2025Article
- Article
- Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.Scientific reports · 2024Article
- Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.Korean journal of radiology · 2023Article
- Prediction of Fishman's skeletal maturity indicators using artificial intelligence.Scientific reports · 2023Article
- Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs.PloS one · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo 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
methodsWe 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.
resultsCA and all estimated BA showed excellent agreement (ICC ≥0.978,
conclusionContemporary 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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