ArticlePediatric radiology2026
Proof-of-concept comparison of an artificial intelligence-based bone age assessment tool with Greulich-Pyle and Tanner-Whitehouse version 2 methods in a pediatric cohort.
Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Prompting the future: artificial intelligence in pediatric radiology.Pediatric radiology · 2026Article
- Perspectives on the future of artificial intelligence in paediatric radiology.Pediatric radiology · 2026Article
- Bone Age in Endocrinology - Part 1: Methods of Bone Age Assessment.Indian journal of endocrinology and metabolismReview
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12 authors.
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Abstract
backgroundBone age assessment is essential in evaluating pediatric growth disorders. Artificial intelligence (AI) systems offer potential improvements in accuracy and reproducibility compared to traditional methods.
objectiveTo compare the performance of a commercially available artificial intelligence-based software (BoneView BoneAge, Gleamer, Paris, France) against two human-assessed methods-the Greulich-Pyle (GP) atlas and Tanner-Whitehouse version 2 (TW2)-in a pediatric population. MATERIALS AND
methodsThis proof-of-concept study included 203 pediatric patients (mean age, 9.0 years; range, 2.0-17.0 years) who underwent hand and wrist radiographs for suspected endocrine or growth-related conditions. After excluding technically inadequate images, 157 cases were analyzed using AI and GP-assessed methods. A subset of 35 patients was also evaluated using the TW2 method by a pediatric endocrinologist. Performance was measured using mean absolute error (MAE), root mean square error (RMSE), bias, and Pearson's correlation coefficient, using chronological age as reference.
resultsThe AI model achieved a MAE of 1.38 years, comparable to the radiologist's GP-based estimate (MAE, 1.30 years), and superior to TW2 (MAE, 2.86 years). RMSE values were 1.75 years, 1.80 years, and 3.88 years, respectively. AI showed minimal bias (-0.05 years), while TW2-based assessments systematically underestimated bone age (bias, -2.63 years). Strong correlations with chronological age were observed for AI (r=0.857) and GP (r=0.894), but not for TW2 (r=0.490).
conclusionBoneView demonstrated comparable accuracy to radiologist-assessed GP method and outperformed TW2 assessments in this cohort. AI-based systems may enhance consistency in pediatric bone age estimation but require careful validation, especially in ethnically diverse populations.
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