Forensic age estimation based on magnetic resonance imaging of third molars: converting 2D staging into 3D staging

Tobel, Jannick De; Hillewig, Elke; Verstraete, Koenraad

doi:10.1080/03014460.2016.1223884

Cited by 31 publications

(19 citation statements)

References 32 publications

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“…The low field open scanners did not render the highly detailed images necessary for staging and substaging of both the epiphyseal and physeal development, but they allowed assessing individual bone development of the hand/wrist [36,39]. Conversely, to study developing teeth and clavicles, 3T appears to be necessary [35,41,42,[48][49][50][51][52].…”

Section: Magnetic Resonance Imaging Approachesmentioning

confidence: 99%

“…Regarding dental development, the first staging techniques were based on radiographs [56,57]. However, since the cemento-enamel junction is indiscernible using the reported MRI sequences, these staging techniques were said to be inappropriate for MRI [42]. Consequently, an MRI-specific technique was reported (Table 6 of the Supplementary Material) [42,49].…”

Section: Staging Techniques and Statistical Processingmentioning

confidence: 99%

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Magnetic resonance imaging for forensic age estimation in living children and young adults: a systematic review

et al. 2020

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Background The use of magnetic resonance imaging (MRI) in forensic age estimation has been explored extensively during the past decade.Objective To synthesize the available MRI data for forensic age estimation in living children and young adults, and to provide a comprehensive overview that can guide age estimation practice and future research.Materials and Methods MEDLINE, Embase and Web of Science were searched. Additionally, cited and citing articles and study registers were searched. Two authors independently selected articles, conducted data extraction, and assessed risk of bias. Study populations including living subjects up to 30 years were considered.Results Fifty-five studies were included in qualitative analysis and 33 in quantitative analysis. Most studies suffered from bias, including relatively small European (Caucasian) populations, varying MR-approaches and varying staging techniques. Therefore, pooling of the age distribution data was not appropriate.Reproducibility of staging was remarkably lower in clavicles than in any other anatomical structure. Age estimation performance was in line with the gold standard, which uses radiographs, with mean absolute errors ranging from 0.85 to 2.0 years. The proportion of correctly classified minors ranged from 65% to 91%. Multifactorial age estimation performed better than based on a single anatomical site. ConclusionMore multi-factorial age estimation studies are necessary, together with studies testing if the MRI data can safely be pooled. The current review results can guide future studies, help medical professionals to decide on the preferred approach for specific cases, and help judicial professionals to interpret the evidential value of age estimation results.

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Section: Magnetic Resonance Imaging Approachesmentioning

confidence: 99%

Section: Staging Techniques and Statistical Processingmentioning

confidence: 99%

Magnetic resonance imaging for forensic age estimation in living children and young adults: a systematic review

et al. 2020

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“…It is currently unclear if the same staging schemes developed for ionizing radiation based methods can also be used for MRI [22], [23]. Therefore, different MRI based methods have been developed for assessing biological development for each of the three anatomical sites [24]- [26], however, these methods still rely on the notion of discretizing biological development into a number of stages and on subjective visual examination.…”

Section: B Magnetic Resonance Imaging Based Age Estimationmentioning

confidence: 99%

Automatic Age Estimation and Majority Age Classification From Multi-Factorial MRI Data

Štern¹,

Payer

Giuliani³

et al. 2019

IEEE J. Biomed. Health Inform.

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Age estimation from radiologic data is an important topic both in clinical medicine as well as in forensic applications, where it is used to assess unknown chronological age or to discriminate minors from adults. In this paper, we propose an automatic multi-factorial age estimation method based on MRI data of hand, clavicle, and teeth to extend the maximal age range from up to 19 years, as commonly used for age assessment based on hand bones, to up to 25 years, when combined with clavicle bones and wisdom teeth. Fusing age-relevant information from all three anatomical sites, our method utilizes a deep convolutional neural network that is trained on a dataset of 322 subjects in the age range between 13 and 25 years, to achieve a mean absolute prediction error in regressing chronological age of 1.01 ± 0.74 years. Furthermore, when used for majority age classification, we show that a classifier derived from thresholding our regression-based predictor is better suited than a classifier directly trained with a classification loss, especially when taking into account that those cases of minors being wrongly classified as adults need to be minimized. In conclusion, we overcome the limitations of the multi-factorial methods currently used in forensic practice, i.e., dependence on ionizing radiation, subjectivity in quantifying age-relevant information, and lack of an established approach to fuse this information from individual anatomical sites.

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“…Magnetic Resonance Imaging (MRI) may be considered as an alternative to reduce the exposure to ionizing radiation. It was already studied for skeletal age estimation based on ossification stages of the clavicular epiphyses [36,37], hand and/or wrist bone ossifications [38][39][40][41] and the development of third molars [42][43][44]. However future research is necessary to investigate the age prediction performances combining skeletal and dental age estimation parameters obtained from MRI.…”

Section: Discussionmentioning

confidence: 99%

Age estimation combining radiographic information of two dental and four skeletal predictors in children and subadults

et al. 2018

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Improved age estimates may result from combining different age predictors. This study aimed to validate age estimation performances combining the radiographic development of teeth, cervical vertebrae, hand and wrist bones, skull, and mandible. The sampled consisted of 256 individuals aged between 4 and 20 years. Bayes' rule with a multivariate continuation ratio model was applied for the distribution of the dental scores. The additional age information of the skeletal variables was assessed extending the dental model separately and combining the variables. The performances of all models were quantified and compared using mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE). The best performance resulted combining all variables (ME - 0.04 for F and M; MAE 0.91 for F and 0.95 for M; RMSE 1.14 for F and 1.19 for M). To improve performances and minimize radiation doses, the combination of teeth and hand and wrist bones information is recommended.

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Forensic age estimation based on magnetic resonance imaging of third molars: converting 2D staging into 3D staging

Abstract: In age estimation, 3T MRI of third molars could be valuable. Some considerations are, however, necessary to transfer known staging methods to this 3D technique.

Cited by 31 publications

References 32 publications

Magnetic resonance imaging for forensic age estimation in living children and young adults: a systematic review

Magnetic resonance imaging for forensic age estimation in living children and young adults: a systematic review

Automatic Age Estimation and Majority Age Classification From Multi-Factorial MRI Data

Age estimation combining radiographic information of two dental and four skeletal predictors in children and subadults

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