Pediatric chronological age estimation from pelvic radiographs using deep learning: a multicenter retrospective study.

OBJECTIVES: Pelvic bones exhibit multiple secondary ossification centers during maturation, enabling automated age estimation. This study investigated whether a deep learning model could estimate chronological age from pelvic radiographs across the full pediatric age range. MATERIALS AND METHODS: This retrospective study included pelvic radiographs from two hospitals for model development, from patients aged between 0 and 18 years. A DenseNet161 model was trained to classify age into 19 1-year interval categories using chronological age as a reference standard and conditional ordinal regression (CORN) loss. External validation was conducted using two independent cohorts, including 541 radiographs from a different department of the same hospital and 203 from an external hospital. External validation was performed on 744 radiographs from two independent cohorts. Model performance was evaluated using the accuracy of predictions within 1 year of the chronological age (ACC@dist = 1) and the mean absolute error (MAE). RESULTS: A total of 3972 radiographs (median age, 2.5 years; interquartile range, 0-8 years; 2463 males) were included in model development. On the internal test set, the model achieved an MAE of 0.394 years (girls: 0.360; boys: 0.419) and an ACC@dist = 1 of 0.937 (girls: 0.944; boys: 0.931). On the external test sets, the corresponding values were 0.513 years (girls, 0.628; boys, 0.455) and 0.927 (girls, 0.896; boys, 0.943), respectively. CONCLUSION: Automated age estimation in children is feasible from pelvic radiography using a deep neural network. KEY POINTS: Question Pelvic radiographs are commonly obtained in children, yet their potential for automated chronological age estimation across the full pediatric age range remains unexplored. Findings A deep learning model estimated chronological age from pelvic radiographs with a mean absolute error of 0.394 and 0.513 years on internal and external test sets, respectively. Clinical relevance Deep learning-based chronological age estimation from pelvic radiographs is feasible across the full pediatric age range. This approach allows clinically relevant skeletal maturation data to be extracted from imaging already acquired for other indications.