A new analytical cut-off point for determining 18 years of age using MRI on medial clavicular epiphysis

Medico-legal indicators and cut-offs in different age classes through quantitative analysis of epiphyseal fusion segments on knee CT scans

Scientists are interested in determining age in subadults for several forensic purposes. High- resolution instrumental techniques are being increasingly used for age estimation, driven by the need to minimize errors; in this context, several studies have focused on the knee joint, recognized as a potential site for age examination in late adolescence. We analyzed 200 CT scans performed on Russian subjects (106 males and 94 females) between 13 and 20 years, without growth diseases, endocrine disorders, or osteodystrophy. Each subject underwent two scans, one for each leg. Two indicators were measured for each bone (femur, tibia and fibula): the entire length of the epiphyseal scar and the length of the part/s that is/are fused with metaphysis. Intra class Correlation Coefficient (ICC) was performed to evaluate the intra-operator reproducibility. The ratio between the two lengths was calculated for each bone (FemurR, TibiaR and FibulaR). The first aim was to evaluate a correlation between the ratios of the three bones and the three bones treated as a single parameter (given by the sum of the ratios) versus age. The results showed good correlations in both cases (τ a = 0.74, 0.64, 0.57 and 0.67). The second aim was to estimate the cut-offs derived from the sum of the three ratios respect to four age classes (14-15 years: cut-off ≤ 0.63, 15-16 years: cut-off ≤ 1.19, 16-17 years: cut-off ≥ 0.68 and 17-18 years: cut-off ≥ 1.49. The results from this research encourage further studies of the knee joint as an indicator of legal adult age.

An Effective Model for Estimating Age in Unaccompanied Minors under the Italian Legal System

This article presents an effective model for estimating the age of subjects without identification documents, in accordance with Italian legislation covering unaccompanied minors, using instrumental methods recognized by the scientific community for age estimation. A decision-making tree has been developed, in which the first step is a physical examination. If secondary sexual characteristics are fully developed and there are no obvious signs of abnormal growth, dental X-rays are the next step. If the roots of the seven left mandibular teeth between the central incisor and the second molar are completely developed, the focus then moves to the third molar. If the index of the third molar (I_3M) value is less than 0.08, or if third molars are not assessable, the following step is to study the clavicle which, if fully formed, indicates that the subject is an adult with 99.9% probability; otherwise, the probability is 96%. In all other cases (where the I_3M is over 0.08), the probability that the subject has reached 18 years is less than 60%. The research, carried out initially on x-rays of the wrist, teeth and clavicle, highlighted the uselessness of the x-ray of the wrist for determining the age of majority, because in our sample, all subjects with incomplete maturity of hand/wrist bones were under 16 years of age; thus, OPT was necessary anyway. What we propose is a practical, easily feasible, fast, economical, and extremely reliable method, which can be used on Caucasian populations and beyond for multiple forensic purposes.

Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction

Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.