» Articles » PMID: 35738134

Estimating the Age at Death for Forensic Cases Using Quantitative Computed Tomography

Overview
Specialty Forensic Sciences
Date 2022 Jun 23
PMID 35738134
Authors
Affiliations
Soon will be listed here.
Abstract

Estimation of the age at death is an important task for forensic scientists. Although the correlation between age and bone mineral density is already known, including for cadavers, to our knowledge, there are no published studies on age estimation with quantitative computed tomography. Quantitative computed tomography can be used to measure bone mineral density based on the mean computed tomography value of the cancellous bone. As this value cannot be calculated in putrefied cases, we modified quantitative computed tomography to calculate the bone mineral density from regions of the bone with mean computed tomography values of 50-350 Hounsfield units. We aimed to examine whether this method could be used for age estimation. We examined 171 male and 106 female cadavers, some of which were putrefied. We performed univariate linear regression analysis for age at death and bone mineral density. The resultant intercept, slope, and root mean square error were 91.3, - 0.20 (p < 0.0001), and 11.4, respectively, for male cadavers, and 96.1, - 0.23 (p < 0.0001), and 11.0, respectively, for female cadavers. We evaluated this regression formula by using 10-fold cross-validation, resulting in a coefficient of determination of 0.33 for male cadavers and 0.42 for female cadavers. The modified quantitative computed tomography method may be of assistance in estimating age at death, even in putrefied cases.

Citing Articles

A review of methods of age estimation based on postmortem computed tomography.

Barszcz M, Wozniak K Forensic Sci Res. 2025; 10(1):owae036.

PMID: 39990697 PMC: 11839505. DOI: 10.1093/fsr/owae036.


Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae.

Kondou H, Morohashi R, Ichioka H, Bandou R, Matsunari R, Kawamoto M Int J Environ Res Public Health. 2023; 20(6).

PMID: 36981720 PMC: 10049236. DOI: 10.3390/ijerph20064806.