## 1 Introduction
Using a nonprobabilistic artificial neural network, the mean absolute difference achieved between the real and estimated age ranged from 9.19 to 12.03 years, depending on the variables used in the modelling. The use of classic X-ray images is also applicable. [PERSON] et al. in [5] proposed using hand X-ray images to predict bone age. The images were preprocessed (region of interest (ROI) segmentation, standardization and preprocessing of input radiographs) and analyzed by the neural network, GoogLeNet. The solution was validated using 8325 data samples. The accuracy achieved by the classification model was within 2 years (\(<\)98%) and within 1 year (\(<\)90%) for a group of women and men. A similar solution was presented by [PERSON] et al. in [6]. Bone age prediction was performed using a database provided by the Radiological Society of North America (RSNA). Initially, the images were processed: hand masks were extracted and all other objects were removed (using positive mining utilizing U-Net architecture), key points were detected, and the hands were placed on the images in one orientation and size (network inspired by the VGG model). Bone age assessment was performed using two types of convolutional neural network (CNN), regression and classification, as well as image division into three areas (whole hand, carpal bones, metacarpals, and proximal phalanges). The smallest mean absolute error (MAE) was obtained for the ensemble of classification and regression models and the ensemble of regional models. This solution had the best accuracy with an MAE equal to 6.10 months. Human age can also be estimated on the basis of chest images using CNNs. In solution [7], [PERSON] et al. created such a solution based on the DenseNet-169 model, which was pretrained on ImageNet. They used the Chest X-ray dataset from the National Institutes of Health, which includes 10,000 chest images from 30,000 different people. They obtained the following results: for a margin of \(\pm\)4 years, a sensitivity equal to 0.6745 and for \(\pm\)9 years the value was \(-\)0.9441. Similarly, [PERSON] et al. [8] used X-rays to classify gender. The dataset contained 2066 chest X-ray images (1097 women and 969 men). The images were preprocessed and then the features were extracted from them using the following neural networks: AlexNet, VggNet, GoogLeNet, and ResNet (for comparison). The classification was carried out using a support vector machine (SVM) and a random forest. The best result was obtained for the VGGNet-16 + SVM classifier, which had an accuracy of 86.6% with 5 times cross-validation. The VGGNet-16 is a version of VGG family of models (Visual Geometry Group, from the Department of Science and Engineering of Oxford University) with 16 layers (13 convolutional layers and 3 dense layers). Spine X-ray images can also be used to classify gender. [PERSON] et al. in [9] developed a sequential CNN model, trained from scratch using spine images. An accuracy of 88.9% was obtained for the original spine images. Data analysis using the DenseNet-121 model pre-overtrained in the ImageNet data range gave an accuracy of 99% for cervical images and 98% for lumbar images. In [10] [PERSON] et al. proposed a hybrid methodology for gender classification and bone age estimation, using the trained VGG-16 model and the RSNA dataset. They achieved an accuracy of 99% for the gender classification and for age classification they achieved an MAD 0.50 years and an RMS of 0.67 years. In [11] [PERSON] et al. proposed to examine a method based on CNNs and left-hand radiographs of children to determine gender. Using class activation mapping (CAM) they showed that the lower part of the palm around the wrists (wrist) was more important in determining the gender of the child. The model accuracy was 98%, given the unfinished skeleton of the children. Age can also be determined from whole-body CT scans. In [12], [PERSON] et al. presented a solution based on deep hierarchical features. They used data from an anonymous hospital that contained 813 whole-body CT bone images.