A child’s bone age is important for the diagnosis of a wide range of growth disorders. The most often used manual method for bone age assessment (BAA) consists of comparing hand-wrist radiographs with ’ground-truth’ atlasses. This method is criticised for being time-invasive, prone to inter- and intra-observer variability and not applicable to the present-day multicultural population. Therefore, much research has been conducted in creating automated methods for BAA, using machine or deep learning (DL). Instead of using radiographs, dual-energy X-ray absorptiometry (DXA) scans could also be used for BAA, which have the benefit of a lower effective dose. This study focuses on two gaps in current research on automated BAA: developing an automated method for the
use on DXA scans and incorporating ethnic information into the algorithm.
For this purpose, a DL network was constructed and pre-trained on a large data set of radiographs. Transfer learning was adopted to a data set containing DXA scans. The performance of four different models was measured in mean absolute difference (MAD) to observe the effect of adding gender and ethnic information as extra inputs. Final performance was measured on a lock box, which was kept aside during the entire training and tuning process. To gain more insight, regions important for the assessment by the automated model were being visualised using a modified version of Class Activation Mapping (CAM). Furthermore, a comparison was made with software created for automated BAA on radiographs.

Whether or not adding gender and ethnic information as extra inputs did not show a clear effect on the performance. The final performance on the lock box was an MAD of 6.8 months. The activation maps showed that the carpal region was the most important for the automated BAA. The comparison with the radiograph software showed it was not applicable on DXA scans and emphasised the need for a DXA-specific method.

This is the first study that developed an automated BAA method for the use on DXA scans rather than radiographs and the first that incorporates ethnic information inside the algorithm. An MAD of 6.8 months on a totally independent test set (lock box) is comparable with the inter-observer variability of manual BAA and performances reported for state-of-the-art automated BAA methods on radiographs. This method can contribute to reducing radiation exposure and time-intensiveness of the current BAA procedure.