Objective To explore the clinical applicability of a self-developed artificial intelligence bone age assessment model based on a North American dataset for children and adolescents. Methods Based on the Greulich-Pyle (G-P) bone age assessment method, a deep learning-based artificial intelligence bone age assessment model (AIBAA-GP) was constructed using 14 236 left-hand wrist DR images from the 2017 Radiological Society of North America (RSNA) public bone age dataset. Retrospective collection of imaging data from children who underwent left-hand wrist DR imaging at Xiamen Huli District Maternal and Child Health Hospital between September 2018 and September 2022, due to parental requests for bone age assessment, was conducted. A total of 1 709 eligible images were selected, including 867 images of boys and 842 images of girls. The AIBAA-GP model was used to assess bone age. The assessed bone age results were compared with chronological age, and the model's performance across different age groups was evaluated. Results The assessment results of AIBAA-GP model on the 1709 DR images showed statistically significant differences between bone age and chronological age (P<0.05) for boys aged 4, 5, 6, and 7 years and girls aged 3, 4, and 5 years, with mean differences within 1.6 years. By adjusting the mean differences between bone age and chronological age for each age group, the bone age assessment values for the aforementioned groups could be adjusted by -1.16, -1.14, -1.35, -1.29 years for boys and 1.59, -1.52, -1.06 years for girls, respectively. Chronological age was significantly correlated with computer-predicted bone age for boys (r=0.952) and girls (r=0.885). Conclusions The AIBAA-GP algorithm model, based on a North American population dataset, is applicable to a small portion of age groups in Chinese children and adolescents. However, adjustments are required for most age groups before it can be applied in clinical auxiliary diagnosis.
Key words
artificial intelligence /
bone age assessment /
adolescent /
applicability
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