ArticleScientific reports2022
Accuracy and self-validation of automated bone age determination.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- Artificial intelligence algorithm improves radiologists' bone age assessment accuracy.Journal of the Chinese Medical Association : JCMA · 2025Trial
- Bone health in juvenile idiopathic arthritis compared with controls based on a Norwegian observational study.RMD open · 2025Trial
- A prospective cross-sectional diagnostic feasibility study: musculoskeletal ultrasound for pediatric bone age evaluation.Translational pediatrics · 2026Article
- Quantifying human variability in pediatric bone age assessment: a multi-institutional multi-reader study and benchmark for AI evaluation.Pediatric radiology · 2026Article
- Comparative study of the performance of two versions of the same AI tool for pediatric skeletal maturation assessment.European radiology · 2026Article
- High-precision automated bone age: a clinically useful tool in monitoring of treatment effects in children and adolescents.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
- Comparison of BoneXpert and IB-Lab-PANDA automated Bone Age Evaluation in Children With Growth and Puberty Disorders.Journal of the Endocrine Society · 2025Article
- The DRAGON benchmark for clinical NLP.NPJ digital medicine · 2025Article
- AI-powered automated X-ray bone age analyzer for assisting bone age assessment in Chinese children and adolescents: accuracy, consistency, and time efficiency.European journal of pediatrics · 2025Article
- Article
- Frequency of Delayed Puberty in Boys with Contemporary Management of Duchenne Muscular DystrophyJournal of clinical research in pediatric endocrinology · 2024Article
- Article
- Acceleration of skeletal maturation in Central Europe over the last two decades: insights from two cohorts of healthy children.Pediatric radiology · 2024Article
- Validation of an AI-Powered Automated X-ray Bone Age Analyzer in Chinese Children and Adolescents: A Comparison with the Tanner-Whitehouse 3 Method.Advances in therapy · 2024Article
- Deep learning-based identification of spine growth potential on EOS radiographs.European radiology · 2024Article
- Evaluation of height prediction models: from traditional methods to artificial intelligence.Pediatric research · 2024Article
- Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.Pediatric radiology · 2024Article
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- A normative framework for artificial intelligence as a sociotechnical system in healthcare.Patterns (New York, N.Y.) · 2023Review
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5 authors.
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Abstract
The BoneXpert method for automated determination of bone age from hand X-rays was introduced in 2009 and is currently running in over 200 hospitals. The aim of this work is to present version 3 of the method and validate its accuracy and self-validation mechanism that automatically rejects an image if it is at risk of being analysed incorrectly. The training set included 14,036 images from the 2017 Radiological Society of North America (RSNA) Bone Age Challenge, 1642 images of normal Dutch and Californian children, and 8250 images from Tübingen from patients with Short Stature, Congenital Adrenal Hyperplasia and Precocious Puberty. The study resulted in a cross-validated root mean square (RMS) error in the Tübingen images of 0.62 y, compared to 0.72 y in the previous version. The RMS error on the RSNA test set of 200 images was 0.45 y relative to the average of six manual ratings. The self-validation mechanism rejected 0.4% of the RSNA images. 121 outliers among the self-validated images of the Tübingen study were rerated, resulting in 6 cases where BoneXpert deviated more than 1.5 years from the average of the three re-ratings, compared to 72 such cases for the original manual ratings. The accuracy of BoneXpert is clearly better than the accuracy of a single manual rating. The self-validation mechanism rejected very few images, typically with abnormal anatomy, and among the accepted images, there were 12 times fewer severe bone age errors than in manual ratings, suggesting that BoneXpert could be safer than manual rating.
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