Department of Stomatology, The Fourth Medical Centre, Chinese PLA General Hospital, Beijing, China.
Chinese PLA Medical School, Beijing, China.
J Forensic Sci. 2024 Jan;69(1):329-336. doi: 10.1111/1556-4029.15402. Epub 2023 Oct 20.
The human permanent dentition has been commonly used for personal identification due to its uniqueness. Limited research, however, is conducted using 3D digital dental models. We propose to develop a new 3D superimposition method using the contours of human dentition and to further evaluate its feasibility. A total of 270 intraoral scan models were collected from 135 subjects. After a one-year interval, 52 subjects were chosen at random and the secondary intraoral scan models were obtained. The dentition contours of the first and secondary models were extracted to form a resource dataset and a test dataset. Through the application of the iterative nearest point (ICP) algorithm, the test dataset was registered with the resource dataset, and the root mean square error (RMSE) values of the point-to-point distances were calculated. 104 genuine pairs and 13,936 imposter pairs were generated, and in this study, the registration accuracy was 100%. The difference between mean RMSE values for the genuine pair (0.20 ± 0.06 mm) and the minimum RMSE value for the imposter pair (0.83 ± 0.06 mm) was significant in the maxillary arch (p < 0.05). Similarly, in the mandibular arch, the difference between mean RMSE values for the genuine pair (0.22 ± 0.07 mm) and the minimum RMSE value for the imposter pair (0.85 ± 0.08 mm) was significant (p < 0.05). The difference between the RMSE value for the genuine pair in the maxillary and the mandibular arch was significant (p < 0.05). This study indicated the feasibility of dentition contour-based model superimposition and could be considered for personal identification in the future.
人类恒牙列由于其独特性而被广泛用于个人识别。然而,使用 3D 数字牙科模型进行的相关研究有限。本研究旨在提出一种新的基于人类牙列轮廓的 3D 叠加方法,并进一步评估其可行性。共从 135 名受试者中收集了 270 个口内扫描模型。一年后,随机选择 52 名受试者并获得二次口内扫描模型。从第一次和第二次模型中提取牙列轮廓,形成资源数据集和测试数据集。通过应用迭代最近点(ICP)算法,将测试数据集与资源数据集进行注册,并计算点到点距离的均方根误差(RMSE)值。生成了 104 对真实对和 13936 对伪造对,在本研究中,注册准确率为 100%。上颌弓中,真对的平均 RMSE 值(0.20±0.06mm)与假对的最小 RMSE 值(0.83±0.06mm)之间的差异有统计学意义(p<0.05)。同样,在下颌弓中,真对的平均 RMSE 值(0.22±0.07mm)与假对的最小 RMSE 值(0.85±0.08mm)之间的差异也有统计学意义(p<0.05)。上颌弓和下颌弓中真对的 RMSE 值之间的差异有统计学意义(p<0.05)。本研究表明基于牙列轮廓的模型叠加是可行的,将来可考虑用于个人识别。