1
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Jeong K, Seo J, Han M, Jung D. Korean radiographers' awareness, experiences, and education needs in forensic medicine and forensic radiology. Heliyon 2024; 10:e32219. [PMID: 38873674 PMCID: PMC11170207 DOI: 10.1016/j.heliyon.2024.e32219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 04/15/2024] [Accepted: 05/29/2024] [Indexed: 06/15/2024] Open
Abstract
This study assesses the need for education in forensic medicine and forensic radiology among radiographers by investigating the perceptions and experiences of Korean radiographers working in medical institutions. A structured questionnaire was administered to participants, collected, and analyzed. The results showed that despite receiving frequent forensic cases, Korean radiographers face difficulties in taking appropriate measures about forensic radiology due to a lack of awareness and knowledge of its forensic aspects. The participants indicated that university education in forensic medicine and forensic radiology is necessary. Therefore, it is imperative to develop and implement policies for forensic education programs to enhance radiographers' forensic knowledge and capabilities. Universities should conduct courses on forensic radiology and provide continuing education for radiographers working in this field.
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Affiliation(s)
- Kyeonghwan Jeong
- Department of Radiological Science, Daewon University, Jecheon, Republic of Korea
| | - Jeongmin Seo
- Department of Radiological Science, Catholic University of Pusan, Busan, Republic of Korea
| | - Mihyun Han
- Department of Nursing, Keimyung College University, Daegu, Republic of Korea
| | - Dongkyung Jung
- Department of Radiological Science, Daegu Health College, Daegu, Republic of Korea
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2
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Scendoni R, Giuseppe C, Zolotenkova GV, Zolotenkov DD, Rosamaria DV, Giulio D, Recchia L, Cameriere R. Medico-legal indicators and cut-offs in different age classes through quantitative analysis of epiphyseal fusion segments on knee CT scans. Leg Med (Tokyo) 2023; 65:102318. [PMID: 37639821 DOI: 10.1016/j.legalmed.2023.102318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
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.
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Affiliation(s)
- Roberto Scendoni
- Department of Law, Institute of Legal Medicine, University of Macerata, Macerata, Italy.
| | - Campagna Giuseppe
- Department of Medical-Surgical Sciences and Translational Medicine, University of Rome "Sapienza", Rome, Italy
| | - Galina V Zolotenkova
- Department of Forensic Medicine, First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Dmitry D Zolotenkov
- Department of Forensic Medicine, First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - De Vivo Rosamaria
- Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - D'Aguanno Giulio
- Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Laura Recchia
- Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Roberto Cameriere
- Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
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3
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Scendoni R, Cingolani M, Tambone V, De Micco F. Operational Health Pavilions in Mass Disasters: Lessons Learned from the 2023 Earthquake in Turkey and Syria. Healthcare (Basel) 2023; 11:2052. [PMID: 37510493 PMCID: PMC10380084 DOI: 10.3390/healthcare11142052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/30/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023] Open
Abstract
The massive earthquake that hit Turkey and Syria in February 2023 killed tens of thousands of people, and most of the deceased have not yet been identified. Many victims were pulled from the rubble hours or days later, injured and in need of assistance, treatment, and food, and many have not yet been connected with their families. Armed forces, volunteers, technicians, and health workers must cooperate in synergy in these situations to ensure effective interventions and to improve resilience. Based on the lessons learned from the response efforts to this recent natural catastrophe, this brief report proposes, for the first time, an organisational model structured around five functional pavilions that can be safely set up at the edge of a disaster area. Each pavilion should run its own activities to make a vital contribution to the overall coordinated emergency response. Looking to the future, it is extremely important to apply a technical approach that leads to maximum operational synergy at a disaster site and during the first phase of a sudden-onset emergency.
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Affiliation(s)
- Roberto Scendoni
- Department of Law, University of Macerata, 62100 Macerata, Italy
| | | | - Vittoradolfo Tambone
- Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, 00128 Roma, Italy
| | - Francesco De Micco
- Research Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, 00128 Roma, Italy
- Department of Clinical Affair, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Roma, Italy
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4
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Kondou H, Morohashi R, Ichioka H, Bandou R, Matsunari R, Kawamoto M, Idota N, Ting D, Kimura S, Ikegaya H. Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4806. [PMID: 36981720 PMCID: PMC10049236 DOI: 10.3390/ijerph20064806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem computed tomography (CT) examination of 1000 and 500 male and female cadavers, respectively. These CT slices were converted into 3-dimensional images, and only the thoracolumbar region was extracted. Eighty percent of them were categorized as training datasets and the others as test datasets for both sexes. We fine-tuned the ResNet152 models using the training datasets. We conducted 4-fold cross-validation, and the mean absolute error (MAE) of the test datasets was calculated using the ensemble learning of four ResNet152 models. Consequently, the MAE of the male and female models was 7.25 and 7.16, respectively. Our study shows that DNN models can be useful tools in the field of forensic medicine.
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5
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Cameriere R, Scendoni R, Ferrante L, Mirtella D, Oncini L, Cingolani M. An Effective Model for Estimating Age in Unaccompanied Minors under the Italian Legal System. Healthcare (Basel) 2023; 11:healthcare11020224. [PMID: 36673592 PMCID: PMC9859195 DOI: 10.3390/healthcare11020224] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/08/2023] [Accepted: 01/09/2023] [Indexed: 01/15/2023] Open
Abstract
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 (I3M) 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 I3M 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.
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Affiliation(s)
- Roberto Cameriere
- Department of Medicine and Health Sciences, agEstimation Project, University of Molise, via Cesare Gazzani, 86100 Campobasso, Italy
| | - Roberto Scendoni
- Department of Law, Institute of Legal Medicine, agEstimation Project, University of Macerata, via Don Minzoni 9, 62100 Macerata, Italy
- Correspondence: ; Tel.: +39-347-050-9552
| | - Luigi Ferrante
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, via Tronto 10/A, 60020 Ancona, Italy
| | - Dora Mirtella
- Department of Law, Institute of Legal Medicine, agEstimation Project, University of Macerata, via Don Minzoni 9, 62100 Macerata, Italy
| | - Luigi Oncini
- Radiology Unit, Hospital of Macerata, via Santa Lucia 2, 62100 Macerata, Italy
| | - Mariano Cingolani
- Department of Law, Institute of Legal Medicine, agEstimation Project, University of Macerata, via Don Minzoni 9, 62100 Macerata, Italy
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6
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Pigolkin YI, Zolotenkova GV. [Research and development of Sechenov University forensic medicine department within the program «Priority-2030» implementation]. Sud Med Ekspert 2023; 66:5-8. [PMID: 37496474 DOI: 10.17116/sudmed2023660415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
A comprehensive analysis of research and development results of Sechenov University forensic medicine department for the last 5 years (from 2018 to 2022) was performed. The thematic structure and citation indices of scientific publications were presented. The most promising directions of the department's research activities were identified.
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Affiliation(s)
- Yu I Pigolkin
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - G V Zolotenkova
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
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7
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Scendoni R, Kelmendi J, Arrais Ribeiro IL, Cingolani M, De Micco F, Cameriere R. Anthropometric analysis of orbital and nasal parameters for sexual dimorphism: New anatomical evidences in the field of personal identification through a retrospective observational study. PLoS One 2023; 18:e0284219. [PMID: 37134065 PMCID: PMC10155994 DOI: 10.1371/journal.pone.0284219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 03/26/2023] [Indexed: 05/04/2023] Open
Abstract
Orbital and nasal parameters among modern humans show considerable variation, which affects facial shape, and these characteristics vary according to race, region, and period in evolution. The aim of the study was to ascertain whether there are sex differences in the orbital and/or nasal indexes and/or the single measurements used to calculate these in a Kosovar population. The following parameters were taken into consideration: orbital height (OH), orbital width (OW), nasal height (NH), and nasal width (NW). The ratios between orbital index/nasal index (RONI) were calculated. All measurements were obtained from a population sample comprising 408 individuals. The accuracy in sex prediction was 52.86% (CI95% = 45.05%-60.67%) for NW and 64.96% for NH (CI95% = 57.50%- 72.42%). The difference between male and female indexes was statistically significant (P < 0.05). The anthropometric study revealed that only NW and NH are configured as predictors of sexual dimorphism. It could be useful to increase the number of samples to test the discriminant function in other population groups.
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Affiliation(s)
- Roberto Scendoni
- Department of Law, Institute of Legal Medicine (AgEstimation Project), University of Macerata, Macerata, Italy
| | - Jeta Kelmendi
- Faculty of Medicine, Department of Orthodontic, University of Prishtina, Alma Mater Europaea, Campus Rezonanca, Prishtina, Kosovo
| | | | - Mariano Cingolani
- Department of Law, Institute of Legal Medicine (AgEstimation Project), University of Macerata, Macerata, Italy
| | - Francesco De Micco
- Bioethics and Humanities Research Unit, Campus Bio-Medico University of Rome, Rome, Italy
- Department of Clinical Affairs, Campus Bio-Medico University Hospital Foundation, Rome, Italy
| | - Roberto Cameriere
- Department of Forensic Medicine (AgEstimation Project), University of Molise, Campobasso, Italy
- Department of Forensic Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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8
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Wang J, Zhang H, Wang C, Fu L, Wang Q, Li S, Cong B. Forensic age estimation from human blood using age-related microRNAs and circular RNAs markers. Front Genet 2022; 13:1031806. [PMID: 36506317 PMCID: PMC9732945 DOI: 10.3389/fgene.2022.1031806] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/08/2022] [Indexed: 11/23/2022] Open
Abstract
Aging is a complicated process characterized by progressive and extensive changes in physiological homeostasis at the organismal, tissue, and cellular levels. In modern society, age estimation is essential in a large variety of legal rights and duties. Accumulating evidence suggests roles for microRNAs (miRNAs) and circular RNAs (circRNAs) in regulating numerous processes during aging. Here, we performed circRNA sequencing in two age groups and analyzed microarray data of 171 healthy subjects (17-104 years old) downloaded from Gene Expression Omnibus (GEO) and ArrayExpress databases with integrated bioinformatics methods. A total of 1,403 circular RNAs were differentially expressed between young and old groups, and 141 circular RNAs were expressed exclusively in elderly samples while 10 circular RNAs were expressed only in young subjects. Based on their expression pattern in these two groups, the circular RNAs were categorized into three classes: age-related expression between young and old, age-limited expression-young only, and age-limited expression-old only. Top five expressed circular RNAs among three classes and a total of 18 differentially expressed microRNAs screened from online databases were selected to validate using RT-qPCR tests. An independent set of 200 blood samples (20-80 years old) was used to develop age prediction models based on 15 age-related noncoding RNAs (11 microRNAs and 4 circular RNAs). Different machine learning algorithms for age prediction were applied, including regression tree, bagging, support vector regression (SVR), random forest regression (RFR), and XGBoost. Among them, random forest regression model performed best in both training set (mean absolute error = 3.68 years, r = 0.96) and testing set (MAE = 6.840 years, r = 0.77). Models using one single type of predictors, circular RNAs-only or microRNAs-only, result in bigger errors. Smaller prediction errors were shown in males than females when constructing models according to different-sex separately. Putative microRNA targets (430 genes) were enriched in the cellular senescence pathway and cell homeostasis and cell differentiation regulation, indirectly indicating that the microRNAs screened in our study were correlated with development and aging. This study demonstrates that the noncoding RNA aging clock has potential in predicting chronological age and will be an available biological marker in routine forensic investigation to predict the age of biological samples.
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Affiliation(s)
- Junyan Wang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, Hebei, China
| | - Haixia Zhang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, Hebei, China
| | - Chunyan Wang
- Physical Examination Center of Shijiazhuang First Hospital, Shijiazhuang, Hebei, China
| | - Lihong Fu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, Hebei, China
| | - Qian Wang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, Hebei, China
| | - Shujin Li
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, Hebei, China,*Correspondence: Bin Cong, ; Shujin Li,
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, Hebei, China,*Correspondence: Bin Cong, ; Shujin Li,
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9
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Travaini GV, Pacchioni F, Bellumore S, Bosia M, De Micco F. Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191710594. [PMID: 36078307 PMCID: PMC9517748 DOI: 10.3390/ijerph191710594] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 05/29/2023]
Abstract
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.
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Affiliation(s)
| | - Federico Pacchioni
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Silvia Bellumore
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Marta Bosia
- School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Francesco De Micco
- Bioethics and Humanities Research Unit, Campus Bio-Medico University of Rome, 00128 Rome, Italy
- Department of Clinical Affairs, Campus Bio-Medico University Hospital Foundation, 00128 Rome, Italy
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10
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Scendoni R, Ribeiro ILA, Cingolani M, Giovagnoni A, Curzi M, Fedeli P, Cameriere R. A new analytical cut-off point for determining 18 years of age using MRI on medial clavicular epiphysis. Leg Med (Tokyo) 2022; 54:102010. [PMID: 34979460 DOI: 10.1016/j.legalmed.2021.102010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 12/19/2021] [Accepted: 12/24/2021] [Indexed: 11/19/2022]
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.
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Affiliation(s)
- Roberto Scendoni
- Department of Law, Institute of Legal Medicine (AgEstimation Project), University of Macerata, Macerata, Italy.
| | | | - Mariano Cingolani
- Department of Law, Institute of Legal Medicine (AgEstimation Project), University of Macerata, Macerata, Italy
| | - Andrea Giovagnoni
- Department of Radiological Sciences, Ospedali Riuniti, Marche Polytechnic University, Ancona, Italy
| | - Martina Curzi
- Department of Radiological Sciences, Ospedali Riuniti, Marche Polytechnic University, Ancona, Italy
| | | | - Roberto Cameriere
- Department of Forensic Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russian Federation; AgEstimation Project, Department of Forensic Medicine, University of Molise, Italy
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11
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Thurzo A, Kosnáčová HS, Kurilová V, Kosmeľ S, Beňuš R, Moravanský N, Kováč P, Kuracinová KM, Palkovič M, Varga I. Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy. Healthcare (Basel) 2021; 9:1545. [PMID: 34828590 PMCID: PMC8619074 DOI: 10.3390/healthcare9111545] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 11/10/2021] [Accepted: 11/10/2021] [Indexed: 12/11/2022] Open
Abstract
Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia;
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
| | - Helena Svobodová Kosnáčová
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia;
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy Sciences, Dúbravská Cesta 9, 84505 Bratislava, Slovakia
| | - Veronika Kurilová
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovičova 3, 81219 Bratislava, Slovakia;
| | - Silvester Kosmeľ
- Deep Learning Engineering Department at Cognexa, Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovičova 2, 84216 Bratislava, Slovakia;
| | - Radoslav Beňuš
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
- Department of Anthropology, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská dolina Ilkovičova 6, 84215 Bratislava, Slovakia
| | - Norbert Moravanský
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
- Institute of Forensic Medicine, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia
| | - Peter Kováč
- forensic.sk Institute of Forensic Medical Analyses Ltd., Boženy Němcovej 8, 81104 Bratislava, Slovakia; (R.B.); (N.M.); (P.K.)
- Department of Criminal Law and Criminology, Faculty of Law Trnava University, Kollárova 10, 91701 Trnava, Slovakia
| | - Kristína Mikuš Kuracinová
- Institute of Pathological Anatomy, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia; (K.M.K.); (M.P.)
| | - Michal Palkovič
- Institute of Pathological Anatomy, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia; (K.M.K.); (M.P.)
- Forensic Medicine and Pathological Anatomy Department, Health Care Surveillance Authority (HCSA), Sasinkova 4, 81108 Bratislava, Slovakia
| | - Ivan Varga
- Institute of Histology and Embryology, Faculty of Medicine, Comenius University in Bratislava, 81372 Bratislava, Slovakia;
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