Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem computed tomography (CT) examination of 1000 and 500 male and female cadavers, respectively. These CT slices were converted into 3-dimensional images, and only the thoracolumbar region was extracted. Eighty percent of them were categorized as training datasets and the others as test datasets for both sexes. We fine-tuned the ResNet152 models using the training datasets. We conducted 4-fold cross-validation, and the mean absolute error (MAE) of the test datasets was calculated using the ensemble learning of four ResNet152 models. Consequently, the MAE of the male and female models was 7.25 and 7.16, respectively. Our study shows that DNN models can be useful tools in the field of forensic medicine.

An increase in number of unidentified cadavers is a growing problem. To identify these cadavers, a simple objective method is required to estimate cadaveric age. We examined the correlations between postmortem serum prostate-specific antigen levels and cadaveric age to determine whether serum prostate-specific antigen levels can be used in age estimation of unidentified cadavers. Total serum prostate-specific antigen was measured in 140 male autopsy cases aged from 0 to 94 years. The serum prostate-specific antigen levels of cadavers correlated with age at death to the same degree as with the age of living individuals (r = 0.393, P < 0.01). Prostate-specific antigen levels also correlated with prostate weight, but not with psoas muscle index and body mass index. Cause of death did not influence postmortem serum prostate-specific antigen levels. Age estimation based on prostate-specific antigen provides a simple, objective, and rapid method to determine age at death estimation of cadavers, and is expected to greatly contribute to the identification of cadavers.