## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Biological Profile Estimation
#### Age Estimation from Skeletal Structures
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Methods** & **Data** \\ \hline [PERSON] et al., 1995 [92] & Neural Network & 72 hand-wrist radiographs \\ [PERSON] et al., 1995 [93] & Neural Network & 521 hand-wrist radiographs \\ [PERSON] et al., 2000 [94] & Active Shape Model & 57 hand-wrist radiographs \\  & \& Bayesian regression & \\ [PERSON] et al., 2001, & Gibbs random fields, & 1540 hand-wrist radiographs \\
2003 \& 2004 [64; 65; 66] & Active Contours, Wavelets & \\  & \& image processing tools & \\ [PERSON] et al., 2004 [59] & Fuzzy ID3 decision tree & 142 hand-wrist radiographs \\ [PERSON] et al., 2007 [95] & Fuzzy classifiers & 300 hand-wrist radiographs \\ [PERSON] et al., 2007 [96] & Gibbs random fields & 1100 hand-wrist radiographs \\  & \& active contours & \\ [PERSON] et al., 2007 [89] & Neural Network & 909 hand-wrist radiographs \\ [PERSON] et al., 2007 [97] & Fuzzy classification & 720 hand-wrist radiographs \\ [PERSON] et al., 2007 [98] & Fuzzy classification & 205 hand-wrist radiographs \\ [PERSON] et al., 2008 [99] & PSO-based ROI search/feature extraction \& Neural Network & \\ [PERSON] \& [PERSON], 2008 [100] & Adaptive Clustering \& \\  & Neural Network & \\ [PERSON] et al., 2009 [101; 102] & BoneXpert system: Active & 1559 \& 719 hand-wrist \\  & Appearance Model \& PCA & radiographs, respectively \\ [PERSON] et al., 2010 [103] & Snakes, image processing & 106 hand-wrist radiographs \\  & \& geometric features analysis & \\ [PERSON] et al., 2011 [104] & BoneXpert system & 752 hand-wrist radiographs \\ [PERSON] et al., 2012 [80] & Shape descriptors & 14 human 4 th rib CTs \\ [PERSON] et al., 2012 [105] & 2D shape descriptors \& C4.5 & 100 hand-wrist radiographs \\ [PERSON] et al., 2012 [106] & Histogram matching & 32 hand-wrist radiographs \\ [PERSON] et al., 2012 [107] & Fuzzy neural network & 600 hand-wrist radiographs \\ [PERSON] et al., 2014 [108] & Statistical Appearance Model & Hand-wrist radiographs \\  & \& Random Forest & \\ [PERSON] et al., 2014 [60] & Random Forest & 56 MR hand images \\ [PERSON] \& [PERSON], 2016 [109] & Random Forest & 132 MR hand images \\ [PERSON] et al., 2016 [110] & Hidden Markov Models & 360 hand-wrist radiographs \\ [PERSON] et al., 2016 [111] & Feature extractors \& SVM & 1101 hand-wrist radiographs \\ [PERSON] et al., 2016 [112] & Ensemble of classifiers & 135 hand-wrist radiographs \\ [PERSON] et al., 2017 [90] & Mamdani fuzzy inference system & Hand-wrist radiographs \\ [PERSON] et al., 2017 [84] & ConvNet & 200 hand-wrist radiographs \\ [PERSON] et al., 2017 [86] & ConvNet & 8325 hand-wrist radiographs \\ [PERSON] et al., 2017 [91] & ConvNet & 1391 hand-wrist radiographs \\ [PERSON] et al., 2018 [85] & ConvNet & 14,036 hand-wrist radiographs \\ [PERSON] \& [PERSON], 2018 [87] & ConvNet & \(\sim\)12,000 hand-wrist radiographs \\ [PERSON] et al., 2018 [88] & ConvNet & 10,289 hand-wrist radiographs \\ [PERSON] et al., 2019 [113] & ConvNet & 1875 pelvic radiographs \\ [PERSON] et al., 2019 [114] & ConvNet and Random Forest & 328 MR hand images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview of the literature on AI-based approaches for age estimation from skeletal data. References are ordered according to their publication date. in clinical settings, to estimate skeleton maturity and detect abnormalities/diseases, and it is not used in forensic settings for age estimation.) [115]. The estimates of the model, the clinical report, and three human reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model. [PERSON] et al. [84] used a GP method-based DL technique to develop an automatic software system for bone age estimation. Using that software, they estimated the bone age from left-hand radiographs of 200 patients (3-17 years old) using first-rank bone age (only the software), computer-assisted bone age (two radiologists with software assistance), and GP atlas-assisted bone age (two radiologists with GP atlas assistance).