Application of machine-learning methods in age-at-death estimation from 3D surface scans of the adult acetabulum.
Journal:
Forensic science international
PMID:
39476740
Abstract
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor.