ArticleYonsei medical journal2023
Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning.
Article in Yonsei medical journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Deep Learning-Based Bone Age Assessment for Predicting Final Adult Height in Girls With Central Precocious Puberty.Korean journal of radiology · 2026Article
- Transfer Learning From Hand-Trained Deep Learning Models to Estimate Bone Age From Knee Radiographs.Orthopaedic journal of sports medicine · 2026Article
- Clinically Optimized Adult Height Prediction From Key Bone and Pubertal Stages: Prospective Validation to Adult Height.Journal of the Endocrine Society · 2026Article
- Automated Bone Age Assessment and Adult Height Prediction from Pediatric Hand Radiographs via a Cascaded Deep Learning Framework.Journal of medical systems · 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
- Comparative Analysis of Maturation Prediction Methods (Moore, Mirwald, BAUSportJournal of functional morphology and kinesiology · 2025Article
- Comparison of adult height prediction using bone age and body composition for growth assessment in Korean children.Scientific reports · 2025Article
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence for pediatric height prediction using large-scale longitudinal body composition data.Digital healthArticle
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 1 country.
Funding
Abstract
purposeThe appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children. MATERIALS AND
methodsA total of 1678 radiographs from 866 children, for which the interpretation results were consistent between two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults.
resultsThere was a statistically significant correlation between bone age interpreted by the artificial intelligence (AI) program and the reference bone age in the test set simulation (r=0.99,
conclusionWe developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demonstrated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.
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Registered trials
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.