1
|
Bizjak Ž, Robič T. DentAge: Deep learning for automated age prediction using panoramic dental X-ray images. J Forensic Sci 2024. [PMID: 39294554 DOI: 10.1111/1556-4029.15629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Revised: 08/05/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024]
Abstract
Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X-ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine-tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10-20]) to 13.40 years (age group [90-100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real-world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios.
Collapse
Affiliation(s)
- Žiga Bizjak
- Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
| | | |
Collapse
|
2
|
Suzuki S, Ohtani M, Matsuo Y, Fukuda M, Mimasaka S. Age estimation using postmortem computed tomography-based Hounsfield unit values of the palate and mandibular condyle and the Eichner classification. Leg Med (Tokyo) 2024; 69:102446. [PMID: 38640872 DOI: 10.1016/j.legalmed.2024.102446] [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: 02/11/2024] [Revised: 03/17/2024] [Accepted: 04/15/2024] [Indexed: 04/21/2024]
Abstract
Age estimation is important in forensic investigations of unidentified human remains. This study assessed the correlation between age and Hounsfield unit (HU) values of the palate and mandibular condyle based on postmortem computed tomography (CT) and analyzed the influence of occlusal support in developing an age estimation method for Japanese individuals, including older adults. The sample consisted of a training dataset (357 cadavers) and a validation dataset (300 cadavers) that underwent postmortem CT. Three measurements were selected: the respective HU values of the palate and mandibular condyle and the Eichner classification. The correlation coefficients between age and HU values were also evaluated. Multiple stepwise regression analysis was performed to evaluate the significance of four parameters (sex, respective HU values of the palate and mandibular condyle, and the Eichner classification) for age estimation and to determine the best age estimation formula. In the validation tests, inaccuracy and bias were calculated for the groups aged ≥65 or <65 years. Significant correlations between age and HU values of the palate and mandibular condyle were observed, regardless of sex. In multiple stepwise regression analysis, all variables except sex were significantly correlated with age. The age estimation formula from the regression analysis was useful, and the validation test exhibited high accuracy, especially in older adults. The HU values of the palate and mandibular condyle and the Eichner classification are useful for age estimation in Japanese individuals.
Collapse
Affiliation(s)
- Shoken Suzuki
- Department of Forensic Sciences, Akita University Graduate School of Medicine, Akita, Japan; Department of Dentistry and Oral Surgery, Akita University Graduate School of Medicine, Akita, Japan.
| | - Maki Ohtani
- Department of Forensic Sciences, Akita University Graduate School of Medicine, Akita, Japan.
| | - Yuhei Matsuo
- Department of Forensic Sciences, Akita University Graduate School of Medicine, Akita, Japan.
| | - Masayuki Fukuda
- Department of Dentistry and Oral Surgery, Akita University Graduate School of Medicine, Akita, Japan.
| | - Sohtaro Mimasaka
- Department of Forensic Sciences, Akita University Graduate School of Medicine, Akita, Japan; Division of Forensic Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan.
| |
Collapse
|
3
|
Warrier V, Shedge R, Garg PK, Dixit SG, Krishan K, Kanchan T. Machine learning and regression analysis for age estimation from the iliac crest based on computed tomographic explorations in an Indian population. MEDICINE, SCIENCE, AND THE LAW 2024; 64:204-216. [PMID: 37670580 DOI: 10.1177/00258024231198917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Age estimation constitutes an integral parameter of identification. In children, sub-adults, and young adults, accurate age estimation is vital on various aspects of civil, criminal, and immigration law. The iliac crest presents as a suitable age marker within these age cohorts, and the modified Risser method constitutes a relatively novel and unexplored method for iliac crest age estimation. The present study attempted to ascertain the applicability of this modified method for age estimation in the Indian population, an aspect previously unexplored, through computed tomographic examination of the iliac crest. Computed tomography scans of consenting individuals undergoing routine examinations of the pelvis/ abdomen for various clinically indicated reasons were collected and scored using the modified Risser stages. Computed tomographic examinations of the iliac crest indicate that the recalibrated method accurately depicts the temporal progression of ossification and fusion changes. Different regression and machine learning models were subsequently derived and/or trained to evaluate the accuracy and precision associated with the method. Amongst the ten regression models derived herein, compound regression exhibited the lowest inaccuracy (4.78 years) and root mean squared error values (5.46 years). Machine learning yielded further reduced error rates, with decision tree regression achieving inaccuracy and root mean squared error values of 1.88 years and 2.28 years, respectively. A comparative evaluation of error computations obtained from regression analysis and machine learning illustrates the statistical superiority of machine learning for forensic age estimation. Error computations obtained with machine learning suggest that the modified Risser method is capable of permitting reliable age estimation within criminal and civil proceedings.
Collapse
Affiliation(s)
- Varsha Warrier
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
| | - Rutwik Shedge
- School of Forensic Sciences, National Forensic Sciences University, Tripura, India
| | - Pawan Kumar Garg
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India
| | - Shilpi Gupta Dixit
- Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, India
| | - Kewal Krishan
- Department of Anthropology, (UGC Centre of Advanced Study), Panjab University, Chandigarh, India
| | - Tanuj Kanchan
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
| |
Collapse
|
4
|
Syed Mohd Hamdan SN, Radzi Z, Abdul Rahim AH, Rahmat RAA, Ibrahim N. Computed tomography study of cranial vault thickness in Malaysian subadult population. Int J Legal Med 2024:10.1007/s00414-024-03276-2. [PMID: 38940946 DOI: 10.1007/s00414-024-03276-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
Cranial vault thickness (CVT) and its variations provide valuable insights into an individual's biological attributes such as age and sex. This paper aimed to assess the correlations of CVT with age and sex and develop a regression model for age estimation in Malaysian subadults using computed tomography (CT) images. A total of 521 CT images (male/female: 279/242; age range: 0-20 years; Malay/Chinese/Indian: 221/145/155) were included in the study. Correlations of CVT measurements with age and sex, including frontal bone thickness (FBT), occipital bone thickness (OBT), left parietal bone thickness (LPBT), and right parietal bone thickness (RPBT) were assessed and regression formulae were developed for age estimation in subadults. A significant correlation between CVT measurements and age was demonstrated (p < 0.001). Age estimation was most accurate in the younger age group (< 2 years) at frontal and occipital, and accuracy decreases in the older age groups. Additionally, sexual dimorphism was evident in the frontal and parietal bone thickness within the age range of 3-6 years and 16-20 years, respectively. In conclusion, the findings suggested CVT measurements could be used to corroborate other age estimation methods for subadults.
Collapse
Affiliation(s)
| | - Zamri Radzi
- Department of Paediatric Dentistry & Orthodontics, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Amir Hazwan Abdul Rahim
- Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Lembah Pantai, Kuala Lumpur, 50603, Malaysia
| | - Rabiah Al-Adawiyah Rahmat
- Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Lembah Pantai, Kuala Lumpur, 50603, Malaysia
| | - Norliza Ibrahim
- Department of Oral and Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Lembah Pantai, Kuala Lumpur, 50603, Malaysia.
| |
Collapse
|
5
|
Azarfar G, Ko SB, Adams SJ, Babyn PS. Deep learning-based age estimation from chest CT scans. Int J Comput Assist Radiol Surg 2024; 19:119-127. [PMID: 37418109 DOI: 10.1007/s11548-023-02989-w] [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: 02/07/2023] [Accepted: 06/14/2023] [Indexed: 07/08/2023]
Abstract
PURPOSE Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on their chest CT scan. Additionally, we investigated whether chest CT estimated age is a more accurate predictor of lung cancer risk compared to chronological age. METHODS To develop our age prediction model, we utilized composite CT images and Inception-ResNet-v2. The model was trained, validated, and tested on 13,824 chest CT scans from the National Lung Screening Trial, with 91% for training, 5% for validation, and 4% for testing. Additionally, we independently tested the model on 1849 CT scans collected locally. To assess chest CT estimated age as a risk factor for lung cancer, we computed the relative lung cancer risk between two groups. Group 1 consisted of individuals assigned a CT age older than their chronological age, while Group 2 comprised those assigned a CT age younger than their chronological age. RESULTS Our analysis revealed a mean absolute error of 1.84 years and a Pearson's correlation coefficient of 0.97 for our local data when comparing chronological age with the estimated CT age. The model showed the most activation in the area associated with the lungs during age estimation. The relative risk for lung cancer was 1.82 (95% confidence interval, 1.65-2.02) for individuals assigned a CT age older than their chronological age compared to those assigned a CT age younger than their chronological age. CONCLUSION Findings suggest that chest CT age captures some aspects of biological aging and may be a more accurate predictor of lung cancer risk than chronological age. Future studies with larger and more diverse patients are required for the generalization of the interpretations.
Collapse
Affiliation(s)
- Ghazal Azarfar
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada.
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Seok-Bum Ko
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Scott J Adams
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada
| | - Paul S Babyn
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada
| |
Collapse
|
6
|
Vodanović M, Subašić M, Milošević DP, Galić I, Brkić H. Artificial intelligence in forensic medicine and forensic dentistry. THE JOURNAL OF FORENSIC ODONTO-STOMATOLOGY 2023; 41:30-41. [PMID: 37634174 PMCID: PMC10473456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.
Collapse
Affiliation(s)
- M Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
| | - M Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - D P Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - I Galić
- School of Medicine, University of Split, Croatia
| | - H Brkić
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
| |
Collapse
|
7
|
Vila-Blanco N, Varas-Quintana P, Tomás I, Carreira MJ. A systematic overview of dental methods for age assessment in living individuals: from traditional to artificial intelligence-based approaches. Int J Legal Med 2023; 137:1117-1146. [PMID: 37055627 PMCID: PMC10247592 DOI: 10.1007/s00414-023-02960-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/31/2023] [Indexed: 04/15/2023]
Abstract
Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.
Collapse
Affiliation(s)
- Nicolás Vila-Blanco
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Departamento de Electrónica e Computación, Escola Técnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| | - Paulina Varas-Quintana
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical Surgical Specialities, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Inmaculada Tomás
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
- Oral Sciences Research Group, Special Needs Unit, Department of Surgery and Medical Surgical Specialities, School of Medicine and Dentistry, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - María J. Carreira
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Departamento de Electrónica e Computación, Escola Técnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain
| |
Collapse
|
8
|
Lo M, Mariconti E, Nakhaeizadeh S, Morgan RM. Preparing computed tomography images for machine learning in forensic and virtual anthropology. Forensic Sci Int Synerg 2023; 6:100319. [PMID: 36852172 PMCID: PMC9958428 DOI: 10.1016/j.fsisyn.2023.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Affiliation(s)
- Martin Lo
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,UCL Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,Corresponding author. UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK.
| | - Enrico Mariconti
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK
| | - Sherry Nakhaeizadeh
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,UCL Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK
| | - Ruth M. Morgan
- UCL Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK,UCL Centre for the Forensic Sciences, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK
| |
Collapse
|
9
|
Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study. PLoS One 2023; 18:e0280523. [PMID: 36656878 PMCID: PMC9851527 DOI: 10.1371/journal.pone.0280523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 01/02/2023] [Indexed: 01/20/2023] Open
Abstract
Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers.
Collapse
|
10
|
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.
Collapse
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;
| |
Collapse
|