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Open Access 26-12-2023 | Magnetic Resonance Imaging | Original Article

MRI segmentation of tooth tissue in age prediction of sub-adults — a new method for combining data from the 1st, 2nd, and 3rd molars

Authors: Mai Britt Bjørk, Øyvind Bleka, Sigrid Ingeborg Kvaal, Tomas Sakinis, Frode Alexander Tuvnes, Heidi Beate Eggesbø, Peter Mæhre Lauritzen

Published in: International Journal of Legal Medicine | Issue 3/2024

Abstract

Purpose

We aimed to establish a model combining MRI volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults and compare the age prediction performance of different combinations of all three molars, internally in the study cohort.

Material and method

We examined 99 volunteers using a 1.5 T MR scanner with a customized high-resolution single T2 sequence. Segmentation was performed using SliceOmatic (Tomovision©). Age prediction was based on the tooth tissue ratio (high signal soft tissue + low signal soft tissue)/total. The model included three correlation parameters to account for statistical dependence between the molars. Age prediction performance of different combinations of teeth for the three molars was assessed using interquartile range (IQR).

Results

We included data from the 1st molars from 87 participants (F/M 59/28), 2nd molars from 93 (F/M 60/33) and 3rd molars from 67 (F/M 45/22). The age range was 14–24 years with a median age of 18 years. The model with the best age prediction performance (smallest IQR) was 46–47-18 (lower right 1st and 2nd and upper right 3rd molar) in males. The estimated correlation between the different molars was 0.620 (46 vs. 47), 0.430 (46 vs. 18), and 0.598 (47 vs. 18). IQR was the smallest in tooth combinations including a 3rd molar.

Conclusion

We have established a model for combining tissue volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults. The prediction performance was mostly driven by the 3rd molars. All combinations involving the 3rd molar performed well.

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Engebretsen L, Steffen K, Bahr R, Broderick C, Dvorak J, Janarv PM, Johnson A, Leglise M, Mamisch TC, McKay D, Micheli L, Schamasch P, Singh GD, Stafford DE, Steen H (2010) The International Olympic Committee consensus statement on age determination in high-level young athletes. Br J Sports Med 44(7):476–484. https://doi.org/10.1136/bjsm.2010.073122CrossRefPubMed

Schmeling A, Grundmann C, Fuhrmann A, Kaatsch HJ, Knell B, Ramsthaler F, Reisinger W, Riepert T, Ritz-Timme S, Rosing FW, Rotzscher 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

Liversidge HM (2015) Controversies in age estimation from developing teeth. Ann Hum Biol 42(4):397–406. https://doi.org/10.3109/03014460.2015.1044468CrossRefPubMed

Timme M, Karch A, Shay D, Ottow C, Schmeling A (2020) The relevance of body mass index in forensic age assessment of living individuals: an age-adjusted linear regression analysis using multivariable fractional polynomials. Int J Legal Med 134(5):1861–1868. https://doi.org/10.1007/s00414-020-02381-2CrossRefPubMedPubMedCentral

Stern D, Payer C, Giuliani N, Urschler M (2019) Automatic age estimation and majority age classification from multi-factorial MRI data. IEEE J Biomed Health Inform 23(4):1392–1403. https://doi.org/10.1109/jbhi.2018.2869606CrossRefPubMed

Bjork MB, Kvaal SI, Bleka O, Sakinis T, Tuvnes FA, Haugland MA, Lauritzen PM, Eggesbo HB (2023) Age prediction in sub-adults based on MRI segmentation of 3rd molar tissue volumes. Int J Legal Med. https://doi.org/10.1007/s00414-023-02977-4CrossRefPubMedPubMedCentral

Bjork MB, Kvaal SI, Bleka O, Sakinis T, Tuvnes FA, Haugland MA, Eggesbo HB, Lauritzen PM (2023) Prediction of age older than 18 years in sub-adults by MRI segmentation of 1st and 2nd molars. Int J Legal Med. https://doi.org/10.1007/s00414-023-03055-5CrossRefPubMedPubMedCentral

Carter K, Worthington S (2015) Morphologic and demographic predictors of third molar agenesis: a systematic review and meta-analysis. J Dent Res 94(7):886–894. https://doi.org/10.1177/0022034515581644CrossRefPubMed

Cantekin K, Sekerci AE, Buyuk SK (2013) Dental computed tomographic imaging as age estimation: morphological analysis of the third molar of a group of Turkish population. Am J Forensic Med Pathol 34:357–362CrossRefPubMed

10.

Sarkodie B, Ofori E, Pambo P (2013) MRI to determine the chronological age of Ghanaian footballers. S Afr J Sports Med 25(3):3. https://doi.org/10.7196/sajsm.487CrossRef

11.

Shi L, Jiang F, Ouyang F, Zhang J, Wang Z, Shen X (2018) DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children. Forensic Sci Int Genet 33:1–9. https://doi.org/10.1016/j.fsigen.2017.11.005CrossRefPubMed

12.

Aanes H, Bleka O, Dahlberg PS, Carm KT, Lehtimaki T, Raitakari O, Kahonen M, Hurme M, Rolseth V (2023) A new blood based epigenetic age predictor for adolescents and young adults. Sci Rep 13(1):2303. https://doi.org/10.1038/s41598-023-29381-7CrossRefPubMedPubMedCentral

13.

Sgheiza V, Liversidge H (2023) Reference and target sample age distribution impacts between model types in dental developmental age estimation. Int J Legal Med 137(2):383–393. https://doi.org/10.1007/s00414-022-02925-8CrossRefPubMed

14.

Liversidge HM, Peariasamy K, Folayan MO, Adeniyi AO, Ngom PI, Mikami Y, Shimada Y, Kuroe K, Tvete IF, Kvaal SI (2017) A radiographic study of the mandibular third molar root development in different ethnic groups. J Forensic Odontostomatol 2(35):97–108

15.

Schmeling A, Reisinger W, Loreck D, Vendura K, Markus W, Geserick G (2000) Effects of ethnicity on skeletal maturation: consequences for forensic age estimations. Int J Legal Med 113(5):253–258. https://doi.org/10.1007/s004149900102CrossRefPubMed

16.

Schmeling A, Olze A, Reisinger W, Geserick G (2001) Age estimation of living people undergoing criminal proceedings. Lancet (London, England) 358(9276):89–90. https://doi.org/10.1016/s0140-6736(01)05379-xCrossRefPubMed

17.

Schmeling A, Reisinger W, Geserick G, Olze A (2006) Age estimation of unaccompanied minors. Part I. General considerations. Forensic Sci Int 159(Suppl 1):S61-64. https://doi.org/10.1016/j.forsciint.2006.02.017CrossRefPubMed

Title: MRI segmentation of tooth tissue in age prediction of sub-adults — a new method for combining data from the 1st, 2nd, and 3rd molars
Authors: Mai Britt Bjørk
Øyvind Bleka
Sigrid Ingeborg Kvaal
Tomas Sakinis
Frode Alexander Tuvnes
Heidi Beate Eggesbø
Peter Mæhre Lauritzen
Publication date: 26-12-2023
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: International Journal of Legal Medicine / Issue 3/2024
Print ISSN: 0937-9827
Electronic ISSN: 1437-1596
DOI: https://doi.org/10.1007/s00414-023-03149-0

EASD 2024

Springer Medicine

MRI segmentation of tooth tissue in age prediction of sub-adults — a new method for combining data from the 1st, 2nd, and 3rd molars

Abstract

Purpose

Material and method

Results

Conclusion

EASD 2024

Springer Medicine

Abstract

Purpose

Material and method

Results

Conclusion

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