Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework

Bone age assessment (BAA) means challenging tasks in forensic science especially in some extreme situations like only skulls found. This study aimed to develop an accurate three-dimensional deep learning (DL) framework at skull CT metadata for BAA and try to explore new skull markers. In this study, retrospective data of 385,175 Skull CT slices from 1,085 patients ranging from 16.32 to 90.56 years were obtained. The cohort was randomly split into a training set (90%, N = 976) and a test set (10%, N = 109). Additional 101 patients were collected from another center as an external validation set. Evaluations and comparisons with other state-of-the-art DL models and traditional machine learning (ML) models based on hand-crafted methods were hierarchically performed. The mean absolute error (MAE) was the primary parameter. A total of 1186 patients (mean age ± SD: 54.72 ± 14.91, 603 males & 583 females) were evaluated. Our method achieved the best MAE on the training set, test set and external validation set were 6.51, 5.70, and 8.86 years in males, while in females, the best MAE were 6.10, 7.84, and 10.56 years, respectively. In the test set, the MAE of other 2D or 3D models and ML methods based on manual features were ranged from 10.12 to 14.12. The model results showed a tendency of larger errors in the elderly group. The results suggested the proposed three-dimensional DL framework performed better than existing DL and manual methods. Furthermore, our framework explored new skeletal markers for BAA and could serve as a backbone for extracting features from three-dimensional skull CT metadata in a professional manner.