## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Biological Profile Estimation
#### Age Estimation from Skeletal Structures
However, it may not be practical in determining ages over 22 years due to the little change in the mean score of ossification. Compared to the existing cubic regression model, their ConvNet model achieves better average performance (MAE = 0.89 and RMSE = 1.21). These results also improve the DL architectures based on left-hand X-ray images, where the MAE values range from 0.54 to 0.80 years [84; 85; 88; 91]. However, although their statistical analysis indicates a high positive correlation between the estimated and real age (r = 0.916; \(p<0.05\)), this number is less accurate than hand X-ray radiographic images methods (r = 0.992; \(p<0.001\)). As an alternative to the age estimation methods based on X-ray images, research in age estimation using MRI has gained tremendous interest in recent years. The interest in developing automatic MRI-based methods for age estimation is determined by addressing the problems of exposure to ionizing radiation, the necessity to define new MRI specific staging systems, and the subjective influence of the examiner [114]. In [PERSON] et al. [60], they used random forests to separately regress chronological age from intensity-based features extracted from 11 selected hand bones of adolescent subjects. A decision tree excluding metacarpal and phalanx information from older subjects served as a heuristic fusion strategy for age estimation, making this method ad hoc and dependent on parameter tuning. In [PERSON] and [PERSON] [109], the capability of RFs for information fusion was explored by allowing it to internally decide from which bones to learn a subject's chronological age. Thus, they treated aging as a global developmental process without the necessity for heuristic fusion schemes, as in [60], or predefined nonlinear functions, as in [115]. Following the current research trend of replacing handcrafted features in random forests with automatically learned ones, in [109] they proposed a ConvNet architecture to combine age information from individual bones in an automatic fashion by letting the architecture learn directly the most relevant features for age estimation. More recently, in [114] the authors present a solution for automatic age estimation from 3D MRI scans of the hand. They evaluate ML methods, as RFs and ConvNets, with different variants of the image information used as input for learning. Trained on a dataset of 328 MRI images, they compare the performance of the different input strategies and demonstrate unprecedented results achieving the state-of-the-art accuracy compared with previous MRI-based methods. For estimating biological age, they obtained a mean absolute error of 0.37 \(\pm\) 0.51 years for the age range of subjects \(\leq\)18 years, i.e., where bone ossification has not yet saturated. Finally, they adapted their best performing method to 2D images, and applied it to a dataset of X-ray images in order to validate their findings, showing that their method is in line with the state-of-the-art methods developed specifically for X-ray data. As conclusion, bone age assessment is one of the most important topics in FA and, specially, in the evaluation of biological maturity of children. It is usually performed by comparing an X-ray of left hand-wrist with an atlas of known sample bones. With the rise of DL, most works currently employ ConvNets to tackle the problem. Age estimation in adults remains a challenge, as well as the development of approaches that integrate hybrid evidence for achieving more confident results (such as X-ray of the left hand and teeth, and physical or psychological examination, to ensure that the subject has reached the legal age).