## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Biological Profile Estimation
#### Age Estimation from Skeletal Structures
The reference bone age was determined by the consensus of two experienced radiologists. The first-rank bone ages determined by the automatic software system showed a 69.5% of concordance rate and significant correlations with the reference bone age (r = 0.992; \(p<\) 0.001). The concordance rates also increased with the use of the automatic software for both reviewers, and the X-ray images evaluation time required by the radiologists were reduced between 18.0% and 40.0%. Their results suggested that automatic software system reliably showed accurate bone age estimations, and appeared to enhance efficiency by reducing evaluation times without compromising diagnostic accuracy. In [86], the authors created a DL system to automatically detect and segment the hand and wrist. They perform an automated bone age assessment with a fine-tuned ConvNet over a set of 4278 female and 4047 male radiographs (with chronological age of 5-18 years old). Their model achieves 57.32% and 61.40% accuracy for the female and male cohorts on held-out test images. Female test radiographs were assigned a bone age within 1 year in 90.39% of cases and within 2 years in 98.11% of cases. Male test radiographs were assigned in 94.18% of cases within 1 year and in 99.0% of cases within 2 years. Attention maps were created which reveal what features the trained model uses to perform bone age assessment. [PERSON] et al. [87] presented a way to use DL for age estimation from a subject's hand X-ray images, employing a set of feature points on the hand. These points have to be defined to serve as a reference to crop a certain region that is informative in terms of aging-induced morphological changes. [PERSON] et al. [88] using their proposed customized neural network architecture trained on 10,289 images of different skeletal age, achieved a test set MAE of 0.637 and 0.536, respectively. Their results support the hypothesis that purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. Automated approaches reproducing the TW2 method can be mainly classified based on whether they use image processing or knowledge-based techniques, and a thorough review can be found at [PERSON] et al. [61]. The majority of the image processing-based methods date back to the 2000s. These methods use hand radiographs of living individuals as knowledge source for training classifiers. In [59], a computing-with-words-based classifier for skeletal maturity assessment is proposed. In [89] the proposal is based on a neural network and a fuzzy filter output. In [90], a fuzzy inference system is used for age assessment. More recently, [PERSON] et al. [91] proposed and tested several DL approaches. In particular, several existing pre-trained ConvNets are employed to assess skeletal bone age automatically, based on the TW2 method and using a dataset with about 1400 X-ray images. The results showed an average discrepancy between the manual and the automatic evaluation of about 0.8 years. They also designed and trained from scratch a ConvNet, which proved to be the most effective and robust solution in assessing bone age across ethnic groups, age ranges and gender. Furthermore, this was the first automated skeletal bone age assessment work tested on a public dataset. The advent and proliferation of ConvNets using X-ray hand radiographs have facilitated new applications to the evaluation of age using other bones. The iliac crest apophysis provides an excellent subject for the application of forensic age diagnostics to the living, particularly for determining age thresholds of 14, 16 and 18 years. For this reason, [PERSON] et al. [113] developed a DL system to perform automatic bone age estimation based on 1875 clinical X-ray pelvic radiological images, particularly for individuals between 10 and 25 years old. It can handle all possible cases of automated skeletal bone age assessment, even for samples from individuals of 19, 20, and 21 years old.