Construction of a Simplified Bone Age Assessment Model in Chinese Children Aged 3 to 18 Years

骨龄 医学 儿科 内科学
作者
Hua‐Hong Wu,Yaqin Zhang,Chengdong Yu,Yang Li,Wen Shu,Tao Li,Guimin Huang,Dongqing Hou,Fangfang Chen,Junting Liu,Shaoli Li,Xinnan Zong
出处
期刊:The Journal of Clinical Endocrinology and Metabolism [Oxford University Press]
卷期号:110 (10): 2926-2937 被引量:1
标识
DOI:10.1210/clinem/dgaf022
摘要

Abstract Context Bone age assessment (BAA) is critical for pediatric endocrinology. Traditional methods are complex and time-consuming, and current computer-aided systems have made progress but still lacking in robustness. Objective Develop simplified BAA methods to aid pediatricians in quick clinical assessments. Design Overall, 5551 left-hand X-ray images were collected from a cross-sectional survey in 2022 and 2023. Bone age (BA) was assessed using the Tanner-Whitehouse 3-China/radius-ulna-short bone method. The linear regression models were constructed with BA as the dependent variable and 13 radius-ulna-short bone bones' grades as independent variables. Setting A cross-sectional survey in Beijing, China. Participants A total of 5551 children aged 3 to 18 years. Main Outcome Measures Model accuracy was evaluated by R², residuals, and root mean square error, and BA with an error margin ≤0.5 years. Results When bone grades were consistent, a single or few bones may serve as proxies for BA, such as metacarpals and phalanges reaching grade 6, the BA for boys and girls were 13.0 to 13.5 years and 10.9 to 11.5 years, respectively. When bone grades were inconsistent, regression models were needed. Comparatively, the 3-bone models have advantages and are proposed for clinically simplified BAA. The simplified 3-bone model (radius, ulna, and metacarpal I) yielded an R² > 0.94 and a root mean square error < 0.5 years. When considering puberty stages, specific bone combination models can further improve assessment accuracy. Conclusion These 3-bone models could be useful for rapid BAA, with improved accuracy when puberty stages is included. Further validation is warranted to test their robustness across populations and scenarios.
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