{Reference Type}: Journal Article
{Title}: Age estimation in Indian soldiers: Maxillary anterior tooth pulp/tooth volume ratio analysis with cone beam computed tomography.
{Author}: Prakash P;
{Journal}: Med J Armed Forces India
{Volume}: 80
{Issue}: 4
{Year}: 2024 Jul-Aug
暂无{DOI}: 10.1016/j.mjafi.2024.04.016
{Abstract}: <B>UNASSIGNED: </B>The objective of this study was to investigate the utility of Cone Beam Computed Tomography (CBCT)-based pulp tooth volume- ratio of maxillary anterior teeth for accurate age estimation. The project aimed to utilize the HOROS software for image analysis and develop prediction models using regression analysis.<BR><B>UNASSIGNED: </B>1800 male patients in the age group of 20 to 40 years were selected, and maxillary anterior teeth were picked. High-resolution CBCT scans were collected, and image analysis in terms of pulp volume (PV), tooth volume (TV), and pulp-volume-to-tooth-volume ratio (PV/TV) was calculated using HOROS software. Simple linear regression analysis was used to develop prediction models correlating the PV/TV with chronological age.<BR><B>UNASSIGNED: </B>PV/TV of all teeth ranged between 0.073 and 0.214. Pearson correlation coefficient was used to evaluate the correlation between the chronological age and the PV/TV. It shows a statistically significant (positive) but low correlation between age and PV/TV 13 and 22 (combined), respectively, and the highest Pearson correlation (0.849) for maxillary canine (13). This study presents four models for age estimation with maximum standard error ranging between 3.5 and 4.3 and an accuracy of 96%.<BR><B>UNASSIGNED: </B>This study illustrates the effectiveness of CBCT-based PV/TV of maxillary anterior teeth for age assessment. Accurate prediction models were constructed by using regression analysis and the HOROS software. These findings enhance the study of forensic odontology and have potential applications in forensic investigations, archaeological research, and legal-age assessment. Further research is necessary to validate and refine the prediction models, expanding their applicability to larger and more diverse population samples.