Age estimation by assessment of pulp chamber volume: a Bayesian network for the evaluation of dental evidence
- 130 Downloads
The present study aimed to investigate the performance of a Bayesian method in the evaluation of dental age-related evidence collected by means of a geometrical approximation procedure of the pulp chamber volume. Measurement of this volume was based on three-dimensional cone beam computed tomography images.
The Bayesian method was applied by means of a probabilistic graphical model, namely a Bayesian network. Performance of that method was investigated in terms of accuracy and bias of the decisional outcomes. Influence of an informed elicitation of the prior belief of chronological age was also studied by means of a sensitivity analysis.
Outcomes in terms of accuracy were adequate with standard requirements for forensic adult age estimation. Findings also indicated that the Bayesian method does not show a particular tendency towards under- or overestimation of the age variable. Outcomes of the sensitivity analysis showed that results on estimation are improved with a ration elicitation of the prior probabilities of age.
KeywordsForensic age estimation Adult age estimation Bayesian approach Bayesian networks Pulp chamber volume narrowing Secondary dentine deposition
The authors wish to thank Rachel Irlam (King’s College London, UK) for proof-reading the document as well as Lorenzo Gaborini (University of Lausanne, Switzerland) for its valuable contribution in the R Code redaction and all users who tested it. Many acknowledgements are also addressed to the anonymous reviewers for their valuable comments on the manuscript.
This work has been kindly supported by the Swiss National Science Foundation (grant no. P2LAP1_164912).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 3.Schmeling A (2013) Forensic age estimation. In: Siegel JA, Saukko PJ (eds) Encyclopedia of forensic sciences, vol 3. Academic Press, Waltham, pp 133–138. https://doi.org/10.1016/B978-0-12-382165-2.00173-2 CrossRefGoogle Scholar
- 5.Pinchi V, Pradella F, Buti J, Baldinotti C, Focardi M, Norelli G-A (2015) A new age estimation procedure based on the 3D CBCT study of the pulp cavity and hard tissues of the teeth for forensic purposes: a pilot study. J Forensic Legal Med 36:150–157. https://doi.org/10.1016/j.jflm.2015.09.015 CrossRefGoogle Scholar
- 9.Johanson G (1971) Age determinations from human teeth. Odontol Revy 22:1–126Google Scholar
- 22.Agematsu H, Someda H, Hashimoto M, Matsunaga S, Abe S, Kim H-J, Koyama T, Naito H, Ishida R, Ide Y (2010) Three-dimensional observation of decrease in pulp cavity volume using micro-CT: age-related change. Bull Tokyo Dent Coll 51(1):1–6. https://doi.org/10.2209/tdcpublication.51.1 CrossRefPubMedGoogle Scholar
- 23.Star H, Thevissen PW, Jacobs R, Fieuws S, Solheim T, Willems G (2011) Human dental age estimation by calculation of pulp–tooth volume ratios yielded on clinically acquired cone beam computed tomography images of monoradicular teeth. J Forensic Sci 56:S77–S82. https://doi.org/10.1111/j.1556-4029.2010.01633.x CrossRefPubMedGoogle Scholar
- 26.De Angelis D, Gaudio D, Guercini N, Cipriani F, Gibelli D, Caputi S, Cattaneo C (2015) Age estimation from canine volumes. Radiol Med doi: https://doi.org/10.1007/s11547-015-0521-5, 120, 8, 731, 736. Springer Milan
- 31.Aykroyd RG, Lucy D, Pollard AM, Solheim T (1997) Regression analysis in adult age estimation. Am J Phys Anthropol 104(2):259–265. https://doi.org/10.1002/(SICI)1096-8644(199710)104:2<259::AID-AJPA11>3.0.CO;2-Z CrossRefPubMedGoogle Scholar
- 41.Thevissen PW, Fieuws S, Willems G (2010) Human dental age estimation using third molar developmental stages: does a Bayesian approach outperform regression models to discriminate between juveniles and adults? Int J Legal Med 124(1):35–42. https://doi.org/10.1007/s00414-009-0329-8 CrossRefPubMedGoogle Scholar
- 43.Fieuws S, Willems G, Larsen-Tangmose S, Lynnerup N, Boldsen J, Thevissen P (2015) 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/s00414-015-1200-8 CrossRefPubMedGoogle Scholar
- 45.Gallidabino M, Weyermann C, Romolo F, Taroni F (2013) Estimating the time since discharge of spent cartridges: a logical approach for interpreting the evidence. Forensic Sci Int 53:41–48Google Scholar
- 47.Taroni F, Biedermann A, Bozza S, Garbolino P, Aitken C (2014) Bayesian networks for probabilistic inference and decision analysis in forensic science, 2nd edn. John Wiley & Sons, ChichesterGoogle Scholar
- 49.R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, WienGoogle Scholar
- 52.Scheuer JL, Black S (2000) Developmental juvenile osteology. Academic Press, LondonGoogle Scholar
- 53.Plummer M, Best N, Cowles K, Vines K (2006) CODA: convergence diagnosis and output analysis for MCMC. R News 6:7–11Google Scholar
- 55.Cameriere R, Cunha E, Wasterlain SN, De Luca S, Sassaroli E, Pagliara F, Nuzzolese E, Cingolani M, Ferrante L (2013) Age estimation by pulp/tooth ratio in lateral and central incisors by peri-apical X-ray. J Forensic Legal Med 20(5):530–536. https://doi.org/10.1016/j.jflm.2013.02.012 CrossRefGoogle Scholar
- 61.Aitken C, Roberts P, Jackson G (2010) Fundamentals of probability and statistical evidence in criminal proceedings.. Practitioner Guide no 1, vol 1. Royal Statistical Society, Avaiable on: http://www.rss.org.uk/Images/PDF/influencing-change/rss-fundamentals-probability-statistical-evidence.pdf, 01.02.2017
- 62.Howson C (2002) Bayesianism in statistics. In: Swinburne R (ed) Bayes’s theorem. Proceedings of the British Academy. Oxford University Press, Oxford, pp 39–69Google Scholar
- 66.European Network of Forensic Scientific Institutes (ENFSI) (2015) ENFSI guideline for evaluative reporting in forensic science: strengthening the evaluation of forensic results across EuropeGoogle Scholar
- 71.Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (eds) (2014) Bayesian data analysis, 3rd edn. Chapman & Hall/CRC, LondonGoogle Scholar