Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?
Journal:
PloS one
PMID:
39446777
Abstract
The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental panoramic radiographs (DPRs) were assumed as postmortem (PM) and antemortem (AM) images, respectively. The dataset contained 1,029 paired PM-AM DPRs from 2000 to 2020. After constructing a database of AM dentition, the degree of similarity was calculated and sorted in descending order. The matched rank of AM identical to an unknown PM was measured by extracting candidate groups (CGs). The percentage of rank was calculated as the success rate, and similarity scores were compared based on imaging time intervals. The matched AM images were ranked in the CG with success rates of 83.2%, 72.1%, and 59.4% in the imaging time interval for extracting the top 20.0%, 10.0%, and 5.0%, respectively. The success rates depended on sex, and were higher for women than for men: the success rates for the extraction of the top 20.0%, 10.0%, and 5.0% were 97.2%, 81.1%, and 66.5%, respectively, for women and 71.3%, 64.0%, and 52.0%, respectively, for men. The similarity score differed significantly between groups based on the imaging time interval of 17.7 years. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in effectively reducing the size of AM CG in identifying humans.