Novel data mining-based age-at-death estimation model using adult pubic symphysis 3D scans

Abstract

The paper introduces a novel age-at-death estimation model based on Convolutional Neural Network (CNN). The model uses 3D scan of human pubic symphysis as an input and estimates the age-at-death of the individual as an output.

The Mean Absolute Error (MAE) of this model is about 10.6 years for individuals between 18 and 92 years of age-at-death. Moreover, the results of the study indicate that pubic symphysis can be used to estimate the age of individuals across the entire age range.

The study involved a sample of 483 bone scans collected from 374 individuals (from which 109 individuals provided both left and right pubic symphysis).

Keywords

• 3-D scans
• convolutional neural network
• estimation models
• mean absolute error
• model use
• model-based OPC
• data mining
• 3D modeling