Automatic identification of individuals using deep learning method on panoramic radiographs - Akifumi Enomoto; Atsushi-Doksa Lee; Miho Sukedai; Takeshi Shimoide; Ryuichi Katada; Kana Sugimoto; Hiroshi Matsumoto (Elsevier, 2023)
Abstract Background/purpose: The dentition shows individual characteristics and dental structures are stable with respect to postmortem decomposition, allowing the dentition to be used as an effective tool in forensic dentistry. We developed an automatic identification system using panoramic radiographs (PRs) with a deep learning method. Materials and methods: In total, 4966 PRs from 1663 individuals with various changes in image characteristics due to various dental treatments were collected. In total, 3303 images were included in the data set used for model training. Vgg16, Vgg19, ResNet50, ResNet101, and EfficientNet models were applied for identification. The precision curves were evaluated. Results: The matching precision rates of all models (Vgg16, Vgg19, ResNet50, ResNet101, and EfficientNet) were examined. Vgg16 was the best model, with a precision of around 80-90% on 200 epochs, using the Top-N metrics concept with 5-15 candidate labels. The model can successfully identify the individual even with low quantities of dental features in 5-10 s. Conclusion: This identification system with PRs using a deep learning method appears useful. This identification system could prove useful not only for unidentified bodies, but also for unidentified wandering elderly people. This project will be beneficial for police departments and government offices and support disaster responses.