Sex-estimation method for three-dimensional shapes of the skull and skull parts using machine learning

Sex estimation is an indispensable test for identifying skeletal remains in the field of forensic anthropology. We developed a novel sex-estimation method for skulls and several parts of the skull using machine learning. A total of 240 skull shapes were obtained from postmortem computed tomography scans. The shapes of the whole skull, cranium, and mandible were simplified by wrapping them with virtual elastic film. These were then transformed into homologous shape models. Homologous models of the cranium and mandible were segmented into six regions containing well-known sexually dimorphic areas. Shape data were reduced in dimensionality by principal component analysis (PCA) or partial least squares regression (PLS). The components of PCA and PLS were applied to a support vector machine (SVM), and the accuracy rates of sex estimation were assessed. High accuracy rates in sex estimation were observed in SVM after reducing the dimensionality of data with PLS. The rates exceeded 90 % in two of the nine regions examined, whereas the SVM with PCA components did not reach 90 % in any region. Virtual shapes created from very large and small scores of the first principal components of PLS closely resembled masculine and feminine models created by emphasizing the shape difference between the averaged shape of male and female skulls. Such similarities were observed in all skull regions examined, particularly in sexually dimorphic areas. Estimation models also achieved high estimation accuracies in newly prepared skull shapes, suggesting that the estimation method developed here may be sufficiently applicable to actual casework.