ArticleKorean journal of radiology2023
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.
Article in Korean journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 25 citations in OpenAlex.
- Artificial intelligence algorithm improves radiologists' bone age assessment accuracy.Journal of the Chinese Medical Association : JCMA · 2025Trial
- Agreement between three state-of-the-art deep learning bone age estimation models and chronological age in a large contemporary pediatric cohort.Pediatric radiology · 2026Article
- Comparison of Two Artificial Intelligence Programs for Bone Age Assessment Using Hand-Wrist Radiographs in Korean Children and Adolescents Undergoing Orthodontic Diagnosis.Children (Basel, Switzerland) · 2026Article
- Deep Learning-Based Bone Age Assessment for Predicting Final Adult Height in Girls With Central Precocious Puberty.Korean journal of radiology · 2026Article
- Preliminary study on ultrasound-based quantitative assessment of epiphyseal ossification angle for bone age evaluation in children.Quantitative imaging in medicine and surgery · 2026Article
- Automatic bone age assessment: a deep learning case study on the Brazilian population with a supporting mobile application prototype.Scientific reports · 2026Article
- A comparison of two artificial intelligence-based methods for assessing bone age in Turkish children: BoneXpert and VUNO Med-Bone Age.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Automatic bone age assessment: a Turkish population study.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Can AI-Based ChatGPT Models Accurately Analyze Hand-Wrist Radiographs? A Comparative Study.Diagnostics (Basel, Switzerland) · 2025Article
- Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner-Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic Evaluation of Bone Age Using Hand Radiographs and Pancorporal Radiographs in Adolescent Idiopathic Scoliosis.Diagnostics (Basel, Switzerland) · 2025Article
- A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year.International journal of legal medicine · 2025Article
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
- Doctor simulator: Delta-Age-Sex-AdaIn enhancing bone age assessment through AdaIn style transfer.Pediatric radiology · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- An accurate pediatric bone age prediction model using deep learning and contrast conversion.Ewha medical journal · 2024Article
- Development of a New Korean Standard Bone Age Model: A Paradigm for AI-Powered Pediatric Imaging.Korean journal of radiology · 2023Article
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. MATERIALS AND
methodsA convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model).
resultsCompared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months;
conclusionThe Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.