Doctoral Dissertation Research: Estimating adult age-at-death from the pelvis

Skeletal age estimation methods are applied to gain insights into past and present populations and individual lives. Existing estimation methods are based, in general, on the visible wear of joint surfaces that occurs with age. This doctoral dissertation research project expands current age-at-death estimation capabilities by generating and validating new methods of age estimation based on multiple joint surfaces in the pelvis, using scanning technology and deep learning models. The results of this project help to 1) reduce subjectivity in estimating age based on skeletal markers, 2) provide consistent results in estimation of age by practitioners regardless of degree of experience, and 3) increase the precision of age range estimates for older adult populations. The project supports undergraduate and graduate mentoring, training, and research opportunities in STEM, the development of open-source deep learning models, and online training workshops for practitioners, researchers, and general education communities. Traditional qualitative methods for age estimation are problematic in that they are heavily reliant on the practitioners’ level of experience for accurate assessment of the changes that occur with age. Additionally, traditional methods cannot differentiate age-related features in older adults (e.g., individuals over 50 years), resulting in broad age ranges that do not allow a detailed reconstruction of adult life history in relationship to aspects of population structure, health, and the environment. This project applies statistical analyses and AI modeling to measurements from 3D scans and photographs taken of the pelvic joints and provides an important methodological resource for standardizing skeletal methods for age estimation. Improved age estimation methods enhance the understanding of past and present population demographics and enable a more thorough understanding of senescent changes to pelvic joint surfaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.