Skeletal age-at-death estimation from the acetabulum based on a convolutional neural network

The paper presents an age-at-death estimation model based on artificial neural networks with no explicit feature extraction, thus, completely eliminating the need for expert knowledge. As input information, it uses a 3D surface scan of the acetabulum, and as the output, it provides an estimated age-at-death. This study is based on a heterogeneous multipopulational database composed of 943 adult ossa coxae coming from 380 males and 327 females. The mean absolute error of our model for this database is about 12.4 years. The correlation coefficient between actual and estimated age-at-death is 0.6. This clearly demonstrates that our model captures age-related morphological changes of the shape and surface of the acetabulum.