Estimation of population affinity using cranial measurements acquired in multidetector computed tomography images of Japanese and Malay individuals.

It is imperative in a forensic investigation to determine the identity of an unidentified corpse, for which a crucial starting point is to establish population affinity as part of the biological profile supplied by the forensic anthropologist. The present study investigates the feasibility of using multidetector computed tomography (MDCT) images to quantify craniometric variation between Japanese and Malay populations relative to the estimation of population affinity in a forensic context. The Japanese and Malay samples comprise MDCT scans of 252 (122 female; 130 male) and 182 (84 female; 98 male) adult individuals, respectively. A total of 18 measurements were acquired, and two machine learning methods (random forest modeling, RFM; support vector machine, SVM) were applied to classify population affinity. The accuracy of the two-way pooled-sex model was 88.0% for RFM and 94.5% for SVM, respectively. The four-way population and sex model produced an overall classification accuracy of 81.3% for RFM and 91.7% for SVM. The sex-specific models of population affinity showed correct rates of classification of more than 90% in both females (90.8% for RFM and 97.6% for SVM) and males (91.2% for RFM and 97.4% for SVM). Our findings clearly indicate that the cranial measurements acquired in MDCT images can be used for the forensic classification of Japanese and Malay individuals and thus serve as a reference for forensic anthropologists attempting to identify unidentified remains.