Third molar maturity index and legal age in different ethnic populations: Accuracy of Cameriere’s method

Micco, Francesco De; Martino, Federica; Palacio, Luz Andrea Velandia; Cingolani, Mariano; Campobasso, Carlo Pietro

doi:10.1177/0025802419900686

Cited by 16 publications

(4 citation statements)

References 43 publications

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“…Regarding the Skeletal age estimation for forensic purposes, we consider ourselves useful for the direction of the 3D CNN on particular areas of the head and neck. Various experts published research on age estimation by measuring open apices in teeth, stage of teeth eruption, frequently of third molars or canine tooth/pulp ratio [6,17,18,20,21,[23][24][25]27,29,31]. In general, teeth are frequently used for age assessment, but they are not the only structures in the skull to be considered.…”

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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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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Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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“…Ethnicity assessment and identification of skeletal remains are important and difficult tasks for forensic scientists with significant ethical and legal implications [ 1 ]. Numerous methods using craniofacial measurements and indexes are available and useful for these purposes [ 2 – 4 ]. The manner of determining the parameters needed for the estimation of these directories depends on the sort of samples used [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Anthropometric analysis of orbital and nasal parameters for sexual dimorphism: New anatomical evidences in the field of personal identification through a retrospective observational study

et al. 2023

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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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“…This method for the staging-age assignment is gender- and population-specific based on reference studies with corresponding reference tables [ 14 , 15 , 16 ]. This reference ensures a differentiated view of tooth development and associated stages according to population- and nutrition-specific criteria [ 17 , 18 , 19 ]. The mean ages assigned to the stages are given with the respective standard deviation and indicate a minimum and maximum age in each case.…”

Section: Introductionmentioning

confidence: 99%

Forensic Dental Age Estimation: Development of New Algorithm Based on the Minimal Necessary Databases

Bjelopavlovic

Zeigner²,

Hardt³

et al. 2022

JPM

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Objectives Dental age determination relies on the presence of wisdom teeth, which can be assigned to specific age ranges according to their stage of development. The purpose of this study is to highlight the applicability of the Demirjian staging of layman compared to expert, as well as the inclusion of all four wisdom teeth in the overall assessment, in order to emphasize and critically highlight a precise age estimation in clinical practice, especially in the case of agenesis or the presence of less than all four wisdom teeth. Material and Methods: In this study, dental age determination is performed and compared by a trained layperson and an expert using 385 orthopantomograms. The radiographs of known chronological age from male patients in the age range of 11–22 years were acquired from the University Medical Center Mainz. All four wisdom teeth were radiologically viewed if present. Demirjian staging with stages A–H was applied, and regression analysis was performed. Results: The relationship between mineralization of wisdom teeth (18, 28, 38 and 48) and age was linear for all teeth (p < 0.01), except for tooth 18 (p = 0.02). Comparing the prediction of the four teeth individually revealed that the lower teeth gave better predictions than the upper ones (R2 ≥ 0.50 vs. R2 < 0.50). Conclusions: For clinical use, the mandibular wisdom teeth should be preferred when performing dental age estimation using the Demirjian staging method. As a result of the present analysis, two ways of determining dental age by wisdom teeth can be suggested. One is to take only tooth 38, with the formula: age = 3.3 + 0.73 × mineralization of tooth_38. The second recommendation would be to take tooth_48. If both are unavailable, the formula would be age = −0.5 + 0.94 × mineralization tooth_28. Utilizing tooth 18 would not lead to more precise results.

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Third molar maturity index and legal age in different ethnic populations: Accuracy of Cameriere’s method

Cited by 16 publications

References 43 publications

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Anthropometric analysis of orbital and nasal parameters for sexual dimorphism: New anatomical evidences in the field of personal identification through a retrospective observational study

Forensic Dental Age Estimation: Development of New Algorithm Based on the Minimal Necessary Databases

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