Development of an age estimation method for bones based on machine learning using post-mortem computed tomography images of bones

Objectives: Age estimation from bones plays a major role in the identification of skeletal remains. We present a novel age estimation method developed through the application of machine learning (ML) to post-mortem computed tomography (PMCT) images of bones. Materials and Methods: This study used PMCT images of the vertebral body, ischial tuberosity, iliac crest, and femur, which were transformed into homologous models. Two-dimensional discrete wavelet transform (2D-DWT) was conducted to extract high-frequency components. Dimensionality reductions of the prepared data arrays were conducted with principal component analysis and partial least squares regression (PLS). The known ages and scores of the principal components were supplied to ridge regression, least absolute shrinkage and selection operator regression, and support vector regression with a linear kernel or a radial basis function kernel. A 10-fold double-looped cross-validation was conducted and estimation accuracies were verified with the mean absolute errors and correlation coefficients (r) between the actual and estimated ages. Results and Conclusion: Preprocessing with 2D-DWT and PLS obtained good results. Of the ML methods examined, support vector regression with radial basis function kernel achieved the highest accuracy, with an optimum mean absolute error and r of 7.92 (male vertebral body) and 0.837 (female ischial tuberosity), respectively. The method developed in this study could be used as a rapid, accurate, and objective tool for identifying both skeletal remains and non-skeletonized cadavers.