Age estimation using canine pulp volumes in adults: a CBCT image analysis

Int J Legal Med. 2019 Nov;133(6):1967-1976. doi: 10.1007/s00414-019-02147-5. Epub 2019 Aug 30.

Abstract

Secondary dentine deposition is responsible for the decrease in the volume of the pulp cavity with age. Therefore, the volume of the pulp cavity can be considered as a predictor for estimating age. The aims of this study were to investigate the relationship strength between canine pulp volumes and chronological age from homogenous (approximately equal numbers of individuals in each age range) age distribution and to assess the effect of sex as predictor in age estimation. This study was performed on 719 subjects of Pakistani origin. Cone beam computed tomography images of 521 left maxillary and 681 left mandibular canines were collected from 368 females and 349 males aged from 15 to 65 years. Planmeca Romexis® software was used to trace the outline of the pulp cavity and to calculate pulp volumes. Regression analysis was performed to assess the correlation between pulp volumes considering with and without sex as a predictor with chronological age. The obtained results showed that mandibular canine pulp volume and sex have the highest predictive power (R2 = 0.33). The relationship between mandibular canine pulp volume and sex with chronological age demonstrates an odd S-shaped non-linear relationship. A statistically significant difference in volumes of pulp was found (p = 0.000) between males and females. The conclusion was that predictions using the pulp volume of the mandibular canine and sex produced the best estimates of chronological age.

Keywords: Age estimation; Canine pulp volumes; Cone beam computed tomography; Forensic odontology; Homogenous age distribution.

MeSH terms

  • Adolescent
  • Adult
  • Age Determination by Teeth / methods*
  • Aged
  • Cone-Beam Computed Tomography*
  • Cuspid / diagnostic imaging*
  • Dental Pulp / diagnostic imaging*
  • Dental Pulp / growth & development*
  • Female
  • Forensic Dentistry
  • Humans
  • Image Processing, Computer-Assisted
  • Male
  • Middle Aged
  • Pakistan
  • Young Adult