Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model

Abstract

ObjectivesTo improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model.Materials and methodsLateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed.ResultsA total of 3508 lateral elbow radiographs (mean age 9.8 +/- 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91.ConclusionThe olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model.Clinical relevance statementThis AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods.Key Points...