Top

Published in:

01-11-2021 | Magnetic Resonance Imaging | Original Article

Forensic age estimation based on magnetic resonance imaging of the proximal humeral epiphysis in Chinese living individuals

Authors: Ting Lu, Li-rong Qiu, Bo Ren, Lei Shi, Fei Fan, Zhen-hua Deng

Published in: International Journal of Legal Medicine | Issue 6/2021

Abstract

Forensic age estimation in living individuals is mainly based on radiological features, but direct radiography and computed tomography lead to a rise in ethical concerns due to radiation exposure. Thus, the contribution of magnetic resonance imaging (MRI) to age estimation of living individuals is a subject of ongoing research. In the current study, MRIs of shoulder were retrospectively collected from a modern Chinese Han population and data from 835 individuals (599 males and 236 females) in the age group 12 to 30 years were obtained. A staging technique based on (Schmidt et al. Int J Legal Med 121(4):321-324, 2007) and (Kellinghaus et al. Int J Legal Med 124(4):321–325, 2010) was used and all images were evaluated with T1-wieghted turbo spin echo (T1-TSE) sequence and T2-weighed fat suppression (T2-FS) sequence. One-sided images were assessed because data from both sides were considered coincidental, as no significant differences were found (P > 0.05). Two MRI sequences were evaluated separately and subsequently compared. Regression models and supportive vector classification (SVC) models were established accordingly. The intraobserver and interobserver agreement levels were good. Compared with T1-TSE sequence, the R² values of T2-FS sequence were generally higher, while the mean absolute deviation (MAD) values were slightly lower. For T2-FS sequence, the MAD value was 1.49 years in males and 2.19 years in females. With two MRI sequences incorporated, the SVC model obtained with 85.7% correctly classified minors and 96.2% correctly classified adults in males, while 83.3% and 98.0% respectively in females. In conclusion, T2-FS sequence may slightly outperform the T1-TSE sequence in shoulder MRI analysis for age estimation, while shoulder MRIs could be a reliable prediction indicator for the 18-year threshold and two MRI sequences incorporated are encouraged.

Part NG (2011) Radiation protection and safety of radiation sources International Basic Safety Standards.

Schmeling A, Grundmann C, Fuhrmann A, Kaatsch HJ, Knell B, Ramsthaler F, Reisinger W, Riepert T, Ritz-Timme S, Rösing FW, Rötzscher K, Geserick G (2008) Criteria for age estimation in living individuals. Int J Legal Med 122(6):457–460. https://doi.org/10.1007/s00414-008-0254-2CrossRefPubMed

Schmeling A, Geserick G, Reisinger W, Olze A (2007) Age estimation. Forensic Sci Int 165(2–3):178–181. https://doi.org/10.1016/j.forsciint.2006.05.016CrossRefPubMed

Widek T, Genet P, Merkens H, Boldt J, Petrovic A, Vallis J, Scheurer E (2019) Dental age estimation: the chronology of mineralization and eruption of male third molars with 3T MRI. Forensic Sci Int 297:228–235. https://doi.org/10.1016/j.forsciint.2019.02.019CrossRefPubMed

Baumann P, Widek T, Merkens H, Boldt J, Petrovic A, Urschler M, Kirnbauer B, Jakse N, Scheurer E (2015) Dental age estimation of living persons: Comparison of MRI with OPG. Forensic Sci Int 253:76–80. https://doi.org/10.1016/j.forsciint.2015.06.001CrossRefPubMed

Guo Y, Olze A, Ottow C, Schmidt S, Schulz R, Heindel W, Pfeiffer H, Vieth V, Schmeling A (2015) Dental age estimation in living individuals using 3.0 T MRI of lower third molars. Int J Legal Med 129(6):1265–1270. https://doi.org/10.1007/s00414-015-1238-7CrossRefPubMed

Dvorak J, George J, Junge A, Hodler J (2007) Age determination by magnetic resonance imaging of the wrist in adolescent male football players. Brit J Sport Med 41(1):45–52. https://doi.org/10.1136/bjsm.2006.031021CrossRef

George J, Nagendran J, Azmi K (2012) Comparison study of growth plate fusion using MRI versus plain radiographs as used in age determination for exclusion of overaged football players. Br J Sport Med 46(4):273–278. https://doi.org/10.1136/bjsm.2010.074948CrossRef

Hojreh A, Gamper J, Schmook MT, Weber M, Prayer D, Herold CJ, Noebauer-Huhmann IM (2018) Hand MRI and the Greulich-Pyle atlas in skeletal age estimation in adolescents. Skeletal Radiol 47(7):963–971. https://doi.org/10.1007/s00256-017-2867-3CrossRefPubMedPubMedCentral

10.

Timme M, Ottow C, Schulz R, Pfeiffer H, Heindel W, Vieth V, Schmeling A, Schmidt S (2017) Magnetic resonance imaging of the distal radial epiphysis: a new criterion of maturity for determining whether the age of 18 has been completed? Int J Legal Med 131(2):579–584. https://doi.org/10.1007/s00414-016-1502-5CrossRefPubMed

11.

Er A, Bozdag M, Basa CD, Kacmaz IE, Ekizoglu O (2020) Estimating forensic age via magnetic resonance imaging of the distal radial epiphysis. Int J Legal Med 134(1):375–380. https://doi.org/10.1007/s00414-019-02189-9CrossRefPubMed

12.

Štern D, Payer C, Urschler M (2019) Automated age estimation from MRI volumes of the hand. Med Image Anal 58:101538. https://doi.org/10.1016/j.media.2019.101538CrossRefPubMed

13.

Hillewig E, De Tobel J, Cuche O, Vandemaele P, Piette M, Verstraete K (2011) Magnetic resonance imaging of the medial extremity of the clavicle in forensic bone age determination: a new four-minute approach. Eur Radiol 21(4):757–767. https://doi.org/10.1007/s00330-010-1978-1CrossRefPubMed

14.

De Tobel J, Hillewig E, van Wijk M, Fieuws S, de Haas MB, van Rijn RR, Thevissen PW, Verstraete KL (2020) Staging clavicular development on MRI: pitfalls and suggestions for age estimation. J Magn Reson Imaging 51(2):377–388. https://doi.org/10.1002/jmri.26889CrossRefPubMed

15.

Auf der Mauer M, Säring D, Stanczus B, Herrmann J, Groth M, Jopp-van Well E (2019) A 2-year follow-up MRI study for the evaluation of an age estimation method based on knee bone development. Int J Legal Med 133(1):205–215. https://doi.org/10.1007/s00414-018-1826-4CrossRefPubMed

16.

Dedouit F, Auriol J, Rousseau H, Rougé D, Crubézy E, Telmon N (2012) Age assessment by magnetic resonance imaging of the knee: a preliminary study. Forensic Sci Int 217(1–3):232.e231-237. https://doi.org/10.1016/j.forsciint.2011.11.013CrossRef

17.

Fan F, Zhang K, Peng Z, Cui JH, Hu N, Deng ZH (2016) Forensic age estimation of living persons from the knee: comparison of MRI with radiographs. Forensic Sci Int 268:145–150. https://doi.org/10.1016/j.forsciint.2016.10.002CrossRefPubMed

18.

Dallora AL, Berglund JS, Brogren M, Kvist O, Diaz Ruiz S, Dübbel A, Anderberg P (2019) Age assessment of youth and young adults using magnetic resonance imaging of the knee: a deep learning approach. JMIR medical informatics 7(4):e16291. https://doi.org/10.2196/16291CrossRefPubMedPubMedCentral

19.

Mauer MA, Well EJ, Herrmann J, Groth M, Morlock MM, Maas R, Säring D (2020) Automated age estimation of young individuals based on 3D knee MRI using deep learning. Int J Legal Med.https://doi.org/10.1007/s00414-020-02465-z

20.

Saint-Martin P, Rérolle C, Dedouit F, Bouilleau L, Rousseau H, Rougé D, Telmon N (2013) Age estimation by magnetic resonance imaging of the distal tibial epiphysis and the calcaneum. Int J Legal Med 127(5):1023–1030. https://doi.org/10.1007/s00414-013-0844-5CrossRefPubMed

21.

Saint-Martin P, Rérolle C, Dedouit F, Rousseau H, Rougé D, Telmon N (2014) Evaluation of an automatic method for forensic age estimation by magnetic resonance imaging of the distal tibial epiphysis–a preliminary study focusing on the 18-year threshold. Int J Legal Med 128(4):675–683. https://doi.org/10.1007/s00414-014-0987-zCrossRefPubMed

22.

Ekizoglu O, Hocaoglu E, Can IO, Inci E, Aksoy S, Bilgili MG (2015) Magnetic resonance imaging of distal tibia and calcaneus for forensic age estimation in living individuals. Int J Legal Med 129(4):825–831. https://doi.org/10.1007/s00414-015-1187-1CrossRefPubMed

23.

Lu T, Shi L, Zhan MJ, Fan F, Peng Z, Zhang K, Deng ZH (2020) Age estimation based on magnetic resonance imaging of the ankle joint in a modern Chinese Han population. Int J Legal Med 134(5):1843–1852. https://doi.org/10.1007/s00414-020-02364-3CrossRefPubMed

24.

Wittschieber D, Vieth V, Timme M, Dvorak J, Schmeling A (2014) Magnetic resonance imaging of the iliac crest: age estimation in under-20 soccer players. Forensic Sci Med Pat 10(2):198–202. https://doi.org/10.1007/s12024-014-9548-5CrossRef

25.

Ekizoglu O, Inci E, Ors S, Hocaoglu E, Can IO, Basa CD, Kacmaz IE, Kranioti EF (2019) Forensic age diagnostics by magnetic resonance imaging of the proximal humeral epiphysis. Int J Legal Med 133(1):249–256. https://doi.org/10.1007/s00414-018-1952-zCrossRefPubMed

26.

Ekizoglu O, Inci E, Ors S, Kacmaz IE, Basa CD, Can IO, Kranioti EF (2019) Applicability of T1-weighted MRI in the assessment of forensic age based on the epiphyseal closure of the humeral head. Int J Legal Med 133(1):241–248. https://doi.org/10.1007/s00414-018-1868-7CrossRefPubMed

27.

Altinsoy HB, Gurses MS, Bogan M, Unlu NE (2020) Applicability of 3.0 T MRI images in the estimation of full age based on shoulder joint ossification: Single-centre study. Legal Med (Tokyo, Japan) 47:101767. https://doi.org/10.1016/j.legalmed.2020.101767CrossRef

28.

Martínez Vera NP, Höller J, Widek T, Neumayer B, Ehammer T, Urschler M (2017) Forensic age estimation by morphometric analysis of the manubrium from 3D MR images. Forensic Sci Int 277:21–29. https://doi.org/10.1016/j.forsciint.2017.05.005CrossRefPubMed

29.

Vieth V, Schulz R, Heindel W, Pfeiffer H, Buerke B, Schmeling A, Ottow C (2018) Forensic age assessment by 3.0T MRI of the knee: proposal of a new MRI classification of ossification stages. Eur Radiol 28(8):3255–3262. https://doi.org/10.1007/s00330-017-5281-2CrossRefPubMed

30.

De Tobel J, Bauwens J, Parmentier GIL, Franco A, Pauwels NS, Verstraete KL, Thevissen PW (2020) Magnetic resonance imaging for forensic age estimation in living children and young adults: a systematic review. Pediatr Radiol 50(12):1691–1708. https://doi.org/10.1007/s00247-020-04709-xCrossRefPubMed

31.

Schmidt S, Mühler M, Schmeling A, Reisinger W, Schulz R (2007) Magnetic resonance imaging of the clavicular ossification. Int J Legal Med 121(4):321–324. https://doi.org/10.1007/s00414-007-0160-zCrossRefPubMed

32.

Kellinghaus M, Schulz R, Vieth V, Schmidt S, Pfeiffer H, Schmeling A (2010) Enhanced possibilities to make statements on the ossification status of the medial clavicular epiphysis using an amplified staging scheme in evaluating thin-slice CT scans. Int J Legal Med 124(4):321–325. https://doi.org/10.1007/s00414-010-0448-2CrossRefPubMed

33.

Cardoso HF (2008) Age estimation of adolescent and young adult male and female skeletons II, epiphyseal union at the upper limb and scapular girdle in a modern Portuguese skeletal sample. Am J Phys Anthropol 137(1):97–105. https://doi.org/10.1002/ajpa.20850CrossRefPubMed

34.

Coqueugniot H, Weaver TD (2007) Brief communication: infracranial maturation in the skeletal collection from Coimbra, Portugal: new aging standards for epiphyseal union. Am J Phys Anthropol 134(3):424–437. https://doi.org/10.1002/ajpa.20683CrossRefPubMed

35.

Schaefer MC (2008) A summary of epiphyseal union timings in Bosnian males. Int J Osteoarchaeol 18(5):536–545. https://doi.org/10.1002/oa.959CrossRef

36.

Schaefer MC, Black SM (2005) Comparison of ages of epiphyseal union in North American and Bosnian skeletal material. J Forensic Sci 50(4):777–784CrossRef

37.

Schaefer M, Aben G, Vogelsberg C (2015) A demonstration of appearance and union times of three shoulder ossification centers in adolescent and post-adolescent children. J Forensic Radiol Imaging 3(1):49–56. https://doi.org/10.1016/j.jofri.2014.12.006CrossRef

38.

Tirpude DB, Surwade DV, Murkey DP, Wankhede DP, Meena DS (2014) Age determination from epiphyseal union of bones at shoulder joint in girls of central India. J Forensic Med Sic Law 23(1)

39.

Terada Y, Kono S, Tamada D, Uchiumi T, Kose K, Miyagi R, Yamabe E, Yoshioka H (2013) Skeletal age assessment in children using an open compact MRI system. Magn Reson Med 69(6):1697–1702. https://doi.org/10.1002/mrm.24439CrossRefPubMed

40.

De Tobel J, Fieuws S, Hillewig E, Phlypo I, van Wijk M, de Haas MB, Politis C, Verstraete KL, Thevissen PW (2020) Multi-factorial age estimation: a Bayesian approach combining dental and skeletal magnetic resonance imaging. Forensic Sci Int 306:110054. https://doi.org/10.1016/j.forsciint.2019.110054CrossRefPubMed

41.

Boldsen JL, Milner GR, Konigsberg LW, Wood JW, Hoppa RD, Vaupel JW (2002) Transition analysis: a new method for estimating age from skeletons. Paleodemography: Age distributions from skeletal samples. Cambridge University Press, Cambridge, pp 73–106 CrossRef

42.

De Tobel J, Hillewig E, de Haas MB, Van Eeckhout B, Fieuws S, Thevissen PW, Verstraete KL (2019) Forensic age estimation based on T1 SE and VIBE wrist MRI: do a one-fits-all staging technique and age estimation model apply? Euro Radiol 29(6):2924–2935. https://doi.org/10.1007/s00330-018-5944-7CrossRef

43.

Fieuws S, Willems G, Larsen-Tangmose S, Lynnerup N, Boldsen J, Thevissen P (2016) Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure. Int J Legal Med 130(2):489–499. https://doi.org/10.1007/s00414-015-1200-8CrossRefPubMed

Title: Forensic age estimation based on magnetic resonance imaging of the proximal humeral epiphysis in Chinese living individuals
Authors: Ting Lu
Li-rong Qiu
Bo Ren
Lei Shi
Fei Fan
Zhen-hua Deng
Publication date: 01-11-2021
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: International Journal of Legal Medicine / Issue 6/2021
Print ISSN: 0937-9827
Electronic ISSN: 1437-1596
DOI: https://doi.org/10.1007/s00414-021-02653-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Forensic age estimation based on magnetic resonance imaging of the proximal humeral epiphysis in Chinese living individuals

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 6/2021

Sex estimation from the greater sciatic notch: a comparison of classical statistical models and machine learning algorithms

A potential method for sex estimation of human skeletons using deep learning and three-dimensional surface scanning

Quantitative evidence of suppressed TMEM119 microglial immunohistochemistry in fatal morphine intoxications

Identification of antemortem and postmortem fractures in a complex environment by FTIR spectroscopy based on a rabbit tibial fracture self-control model

Postmortem investigation of fatalities following vaccination with COVID-19 vaccines

It is all about the insects: a retrospective on 20 years of forensic entomology highlights the importance of insects in legal investigations