Literature DB >> 34979460

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

Roberto Scendoni1, Isabella Lima Arrais Ribeiro2, Mariano Cingolani3, Andrea Giovagnoni4, Martina Curzi4, Piergiorgio Fedeli5, Roberto Cameriere6.   

Abstract

Evaluation of the ossification of the medial clavicular epiphysis plays a key role in forensic age estimation. The purpose of the present study was to assess a new numerical cut-off at the age of 18 years, taking into consideration Magnetic Resonance (MR) images of the medial clavicular epiphysis. We analyzed 163 MR scans of Italian subjects aged between 14 and 25 years. Using the data obtained we calculated two ratios: REM-1 (ratio between the length of the whole epiphysis and the length of the metaphysis) and REM-2 (ratio between the length of epiphyseal-metaphyseal fusion and the length of the metaphysis). In 68 out of 163 cases it was not possible to measure REM-2. The reproducibility was demonstrated using the Intraclass Correlation Coefficient (ICC) (Cronbach's alpha > 0.80). REM-1 and REM-2 were compared in each category of age (adult and minor) by the Wilcoxon signed-rank test. The cut-off points for measurements of REM-1 and REM-2 were determined by logistic regression. For REM-1, the cut-off scores were 0.83 for all individuals (accuracy = 94.77%) and males (accuracy = 96.05%), and 0.86 for females (accuracy = 92.30%). For REM-2, the cut-off values were 0.40 for all individuals and males (accuracy = 100.00%), and 0.41 for females (accuracy = 100.00%). Finally, receiver operating characteristic (ROC) curves for age classification based on REM-1 and REM-2 were constructed, showing that REM-2 had the highest discriminative power. Thus, a new cut-off model for predicting the age of majority has been introduced, conducting a quantitative analysis thanks to the use of a high-resolution imaging tool.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  18 years; Age estimation; Cut-off point; Logistic regression; Magnetic resonance imaging; Medial clavicular epiphysis

Mesh:

Year:  2021        PMID: 34979460     DOI: 10.1016/j.legalmed.2021.102010

Source DB:  PubMed          Journal:  Leg Med (Tokyo)        ISSN: 1344-6223            Impact factor:   1.376


  1 in total

Review 1.  Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction.

Authors:  Guido Vittorio Travaini; Federico Pacchioni; Silvia Bellumore; Marta Bosia; Francesco De Micco
Journal:  Int J Environ Res Public Health       Date:  2022-08-25       Impact factor: 4.614

  1 in total

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