## 4 Discussion  
The proposed solution achieved satisfying results for the age estimation using the full-body, low-resolution DXA scan with a mean MAE equal to 15.56 months. First, the results obtained were the best among all evaluated models. Second, obtaining an MAE = 0 was not expected due to differences in the body development of young subjects (including pathological changes). For example, hypothyroidism delays skeletal development and thyrotoxicosis accelerates bone maturation [25]. The analyses of samples with greater differences between actual and predicted age could, for example, indicate a connection between the MAE and different disorders, such as the bias in bone mineralization during maturation. Therefore, the difference between chronological age and DXA-related biological age could be potentially important for diagnostics purposes [25; 26]. It it necessary to point out that the prediction of age using single X-ray and DXA images yielded similar results. This might suggest that spatial (geometrical) features are the dominant factor in age prediction using the investigated CNNs. The results of the vanilla gradient algorithm seemed to confirm this statement, as the distribution of the points of  
Figure 8: The results of the vanilla gradient algorithm were placed on top of the original image, and its colour was changed to teal. \begin{table}
\begin{tabular}{l l} \hline
**Images** & **MAE [Months]: Mean and SD** \\ \hline DXA & 15.56 \(\pm\) 1.26 \\ High energy & 15.78 \(\pm\) 1.13 \\ Low energy & 15.43 \(\pm\) 1.04 \\ High air & 15.93 \(\pm\) 1.15 \\ Low air & 15.61 \(\pm\) 1.27 \\ High tissue & 15.74 \(\pm\) 1.13 \\ Low tissue & 15.52 \(\pm\) 1.29 \\ High bone & 15.89 \(\pm\) 1.04 \\ Low bone & 15.47 \(\pm\) 1.04 \\ \hline \end{tabular}
\end{table} 
Table 5: Mean values and standard deviations of MAE obtained in Experiment IV—MobileNetV2 DW, different images. interest, though not perfectly even, lacked any clusters. The issues related to the connection of DXA images and different parameters (e.g., BMD) will be the subject of future work. Different configurations of the models were investigated. Firstly, we investigated different base models, which proved to be efficient for other computer vision tasks in healthcare (MobileNetV2 [17], VGG16 [18], InceptionV3 [19], ResNet50V2 [20], and InceptionResNetV2 [21]). All of these base models showed similar results. However, introducing the depthwise convolution layer instead of the global pooling layer improved the model quality, reducing the MAE value by 1.52 months. Investigated data-augmentation methods also reduced the MAE value (by 0.54 months). The proposed framework was compared with known experiments for the RSNA 2017 dataset. The result of the cross-check analysis showed the importance of adjusting model architecture to the analyzed dataset. For example, the validation results of the retrained attention-based model [13] for our dataset showed much worse results than for the proposed model. The difference in the related mean MAE values was approximately 4 months. In the cross-check we validated our framework for the RSNA 2017 dataset. The results obtained were worse by less than 5 months (mean MAE value). Other different models were proposed for the RSNA 2017 dataset. For example, in [14] the authors achieved a mean MAE equal to 6.05 months. In another paper [15] authors obtained mean MAE equal to 6.38. However, it is important to underline that the images in the RSNA 2017 dataset had a minimal resolution \(512\times 512\) pixels and represented a slightly different domain (only the hand). For comparison purposes only, the size of the hand in images used for this study was, e.g., about \(15\times 15\) pixels. For the MobileNetV2 DW model, the list of five samples from the validation set with the greatest MEA was determined for each fold in two out of five repetitions of Experiment I (a total of 25 samples--14 females and 11 males).