ArticleDiagnostic and interventional radiology (Ankara, Turkey)2025
A comparison of two artificial intelligence-based methods for assessing bone age in Turkish children: BoneXpert and VUNO Med-Bone Age.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- AI-based bone age assessment versus forensic expert panel: agreement, sex bias, and ethical implications.International journal of legal medicine · 2026Article
- Quantifying human variability in pediatric bone age assessment: a multi-institutional multi-reader study and benchmark for AI evaluation.Pediatric radiology · 2026Article
- digiBONE: an automated tool for segmental Greulich-Pyle bone age assessment of Indian children and adolescents.Frontiers in endocrinology · 2026Article
- Population-specific calibration and validation of an open-source bone age AI.Scientific reports · 2025Article
- Potential of Artificial Intelligence for Bone Age Assessment in Iranian Children and Adolescents: An Exploratory Study.Archives of Iranian medicine · 2025Article
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Abstract
purposeThis study aimed to evaluate the validity of two artificial intelligence (AI)-based bone age assessment programs, BoneXpert and VUNO Med-Bone Age (VUNO), compared with manual assessments using the Greulich-Pyle method in Turkish children.
methodsThis study included a cohort of 292 pediatric cases, ranging in age from 1 to 15 years with an equal gender and number distribution in each age group. Two radiologists, who were unaware of the bone age determined by AI, independently evaluated the bone age. The statistical study involved using the intraclass correlation coefficient (ICC) to measure the level of agreement between the manual and AI-based assessments.
resultsThe ICC coefficients for the agreement between the manual measurements of two radiologists indicate almost perfect agreement. When all cases, regardless of gender and age group, were analyzed, an almost perfect positive agreement was observed between the manual and software measurements. When bone age calculations were analyzed separately for boys and girls, no statistically significant differences were found between the two AI-based methods in any subgroup. For boys regardless of age, the ICCs were 0.995 for VUNO and 0.994 for BoneXpert (z = 1.597,
conclusionBoth BoneXpert and VUNO demonstrated high validity in assessing bone age, with no statistically significant differences between the two methods across gender or pubertal status groups. Notably, this study represents the first evaluation of both BoneXpert and VUNO for bone age assessment in Turkish children, highlighting their potential as reliable and clinically relevant tools for this population. CLINICAL SIGNIFICANCE: Investigating the most suitable AI program for the Turkish population could be clinically significant.
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