Artificial intelligence (AI) shows promise in medical imaging, but its performance in obese pediatric bone age requires validation. This study aimed to develop an AI-aided system using digital radiography (DR) and to investigate the relationship between childhood obesity and bone age advancement. In this cross-sectional study, 442 children and adolescents (6-18 years) were enrolled between December 2023 and February 2025. Participants were categorized into normal weight, overweight, and obese groups based on BMI. Left-hand wrist DR images were obtained. Bone age was assessed automatically using United Imaging Intelligence Greulich-Pyle (G-P) atlas AI software and manually verified by two physicians. Physical, metabolic, and hormonal data were collected. Bone age advancement (bone age - chronological age) was analyzed against obesity-related indicators. AI assessment averaged (2.2 ± 0.6) seconds and showed high consistency with manual results (ICC = 0.992). Bone age advancement was greatest in the obese group (1.13 ± 0.88 years), followed by overweight (0.69 ± 0.72 years) and normal weight (0.16 ± 0.72 years). Advanced bone age (≥1 year) occurred in 67.8% of obese participants, significantly higher than the 9.8% in normal weight participants. Bone age advancement positively correlated with BMI, waist circumference, and HOMA-IR. Multivariable logistic regression identified overweight (OR = 3.85) and obesity (OR = 12.63) as independent risk factors for accelerated bone age. ROC analysis indicated HOMA-IR had moderate predictive ability for bone age progression. The AI-assisted DR bone age assessment system demonstrated high efficiency, accuracy, and reliability in obese children, supporting its use in large-scale screening. Obesity, especially with central adiposity and insulin resistance, was strongly associated with accelerated bone age.
Zou et al. (2026) studied this question.