Sex and age are two elements in the establishment of a biological profile for forensic identification. While the pelvic bones are the most ideal structures for sex estimation, the condition of a body is not always ideal due to the nature of death, such as in mass disasters, or postmortem processes. This study utilized CT scans and resultant 3D models of 100 male and 100 female adults of known ages ranging from 18 to 98 years old to collect volumetric and Hounsfield unit measurements of the proximal femur. Equations were created to establish logistic regression models for sex estimation and linear regression models for age estimation. The resultant sex estimation method had an accuracy of 93.5% and utilized the volume of the proximal femur. This study provides three linear regression models for age with an accuracy range of 86%-92% ±12 years. As imaging technologies are increasingly adopted for forensic purposes, the power of 3D data will provide the opportunity for more quantitative and reproducible analyses. The proposed method for sex and age estimation provides a reliable tool that can be utilized in both day-to-day casework and disaster victim identification.
Keywords: Hounsfield unit; age; computed tomography; disaster victim identification; forensic anthropology; forensic science; proximal femur; sex; volumetrics.
© 2019 American Academy of Forensic Sciences.