## 2 Materials and Methods
### Dataset  
The dataset consisted of 1455 samples of the full body DXA scan, but according to the objective of this study we only used examples from young subjects (patients up to 230 months old at the time of the DXA scan). After reduction, the dataset included 910 samples, 570 for males and 370 for females (the mean age was equal to 140.4 \(\pm\) 42.1 months).  
Figure 1: The first six images correspond to low- and high-energy beams passing through different segments of the filter and the patient: (**a**) low air, (**b**) low tissue, (**c**) low bone, (**d**) high air, (**e**) high tissue, (**f**) high bone. Images (**g**–**i**) are a result of the subtraction of high-energy images from low-energy images.  
This dataset is referenced further in the text as the DXA dataset. Considering a small number of samples, the influence of data augmentation on the analyzed deep learning models was tested. The data augmentation operations (selected experimentally) were rotation of the image by an angle of 0 to 5 degrees and random horizontal flip of the image. The augmented dataset is further referenced in the text as the augDXA dataset. The age of the subjects at the time of the DXA scan, which was used as labels, was determined by subtracting the birth date from the date of the DXA scan. All retrospectively used clinical and image data were collected from 2015 to 2020 at Gdansk Medical University. The use of DXA scans in the study was approved by the Independent Bioethics Committee for Scientific Research at the Medical University of Gdansk.  
To obtain an appropriate representation of the data in the training and validation sets, the samples were categorized by age of the subject as follows: group 1--age from 0 to 96 months (170 samples), group 2--age from 97 to 132 months (215 samples), group 3--age from 133 to 156 months (177 samples), group 4--age from 157 to 180 months (168 samples), and group 5--age from 181 to 230 months (184 samples), see Figure 2. The samples in all groups were proportionally divided into a training set (80%) and a validation set (20%) in a five-fold cross-validation experiment. Validation samples were not used during the training. The validation set was used as a test set (during the training the validation set was used only to monitor the training process, it was not used in any decision making, e.g., early stopping, etc.).  
Data Preparation  
Three different variants of the three-channel images were investigated. In the first case the arrays corresponding to high energy were subcontracted from arrays corresponding to low energy for each filter. In the second case, the three-channel images were assembled from the low- and high-energy arrays separately. In the third variant, the images were assembled from three copies of the same array (e.g., three copies of a low tissue array).  
Next, in all cases, the obtained images were normalized globally to a value from \(-1\) to 1, channel-wise. The normalization range was selected on the basis of the model used. Lastly, the images were cropped symmetrically and padded to a size of \(128\times 128\) pixels. Each channel of a single image was padded with a constant value equal to the mean of 24 pixels selected from the background image.  
Figure 2: The data in groups with a similar number of samples were divided proportionally into training and validation datasets based on age.