## 4 Other Methods
### AI-Based Approaches
forage estimation trained on knee MRI scans of 402 volunteers between the ages of 14 and 21. The bone age of male subjects chronologically aged between 14 and 20.5 years could be determined with a MAE of 0.793 years, and the bone age of female subjects aged between 14 and 19.5 years could be determined with a MAE of 0.988 years. Using a cut-off age of 18 years, it was possible to classify subjects with an accuracy of 98.1% for males and 95.0% for females [114]. Other authors proposed an automatic age bone calculation based on MRI images of hands, clavicles, and teeth: a deep CNN was trained on a dataset of 322 subjects in the age range between 13 and 25 years, with the demonstration of mean absolute prediction error in regressing chronological age of 1.01 \(\pm\) 0.74 years [115]. Bone age estimation can be also performed through the cervical vertebral maturation analysis: [PERSON] et al. developed a DL system for automatic calculation and propose a CNN called iCVM [116]; whereas [PERSON] et al. developed eight ML models including 13 anatomic landmarks of the cervical vertebrae as input data, the width-height ratio, width-perpendicular height ratio, concavity ratio, patients' chronological age, and sex. The MAE, round MAE, and RMSE values were, respectively, 0.90, 0.87, and 1.20 [117]. [PERSON] et al. tested the performance of three DL models (VGG19, Inception-V3, and Inception-ResNet-V2) to pelvic radiographs by collecting 962 pelvic X-rays from adolescents and young adults (481 males, 481 females) between 11 and 21 years and divided them into training and validation (80%) and test sets (20%). The performances were compared by calculating the RMSE, MAE, and Bland-Altman plots between the automatic age estimation and the chronological ages: compared to the chronological age, the RMSE and MAE of VGG19 were 1.29 and 1.02 years, respectively; the RMSE and MAE of the Inception-V3 model were 1.17 and 0.82 years, respectively; while the RMSE and MAE of the Inception-ResNet-V2 model were 1.11 and 0.84 years, respectively. Lastly, the lowest mean value of differences between age estimates and the chronological ages by Bland-Altman plots was reached by the Inception-ResNet-V2 model [118]. Pelvic bones play an important role in forensic bone age assessment; however, due to the overlapping of pelvic organs in radiography, which reduces accuracy, there are insufficient applications of machine learning to pelvic X-ray images. In 2022, [PERSON] et al. attempted to overcome this limitation by applying the previously mentioned CNN models to 2164 images after U-Net detected and segmented the regions of interest. The segmented images were then combined with the original images to enhance the key areas and, as a result, reduce the pelvic organ profiles. Hence, the three CNN models were applied both to enhanced and not enhanced images, showing better performances after regional augmentation. For example, the RMSE of the Inception-V3, Inception-ResNet-V2, and VGG19 networks applied on enhanced images were 0.93 years, 1.12 years, and 1.14 years, respectively, while the RMSE became 1.22 years, 1.25 years, and 1.63 years, respectively, on not enhanced images [119]. This result suggests that a good segmentation tool could be an excellent starting point to create new automatic tools, perhaps analyzing anatomical regions that have never been investigated so far. Some segmentation tools have already been tested on different bones and various radiological techniques, ranging from lateral X-ray images of the patella to 3D MRI images of the hip joint [120; 121]. Nowadays, studies are focusing on the most used traditional procedures, including hand-wrist radiographs evaluation, but we cannot exclude that in the future one or more software with a multi-disciplinary approach will exist to best suit each case. The main results of the above studies are collected in Table 5.