Implementation of a personal identification system using alveolar bone images. (February 2023)

• Record Type: Journal Article
• Authors: Fujimoto, Hideko; Kimura-Kataoka, Kaori; Kanayama, Hidekazu; Kitamori, Kanako; Kurihara, Yuichi; Zangpo, Dawa; Takeshita, Haruo
• Is Part Of: Forensic science international. Volume 343(2023)
• Publication Date: 2023-02
• Subjects: Alveolar bone -- Personal identification -- CT image -- Panoramic X-ray image -- Procrustes analysis; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2022.111548
• Languages: English
• ISSNs: 0379-0738

Abstract

Abstract: Objective: In recent years, personal identification has been performed using antemortem panoramic X-ray images and postmortem-CT images. Using these, we have developed a personal identification method that focuses on the alveolar bone. This study examined the effectiveness of this method and aimed to implement a reproducible system. Materials and methods: For personal identification, a total of 633 CT images and panoramic X-ray images belonging to three groups with different conditions were used. These images were 160 sets in the same person group and 96, 820 in the other groups. The similarity of alveolar bone images was calculated using the landmark method of Procrustes analysis. The processes were system implemented and the methodology was validated. Results: The ability to identify between the same person group and other person groups showed 0.9769 as the area under the curve (AUC: ROC curve). At the cutoff value of 4.978, there was no false rejection rate, but false acceptance rate was slightly higher. Conclusion: This method was useful as a screening method for personal identification. In addition, system implementation was efficient and reduced human error. In the future, we aim to realize a more efficient personal identification method using distortion-corrected images and including auto-detective landmarks using deep learning. Highlights: A personal identification method was developed for focusing on the alveolar bone. A total of 633 postmortem-CT images and panoramic X-ray images were used. The system implementation was efficient and reduced human error. The study to the identification of disaster victims and missing persons.