Dental parameter quantification with semi-automatized computational technology for the analysis of human bitemarks

The study objective was to determine dental parameters that characterize human bitemarks and dentitions for biter identification using semi-automatized technology. Sixty-five dental casts and eighteen bitemark photographs were used. Casts were scanned by a 3D laser scanner to generate comparison overlays, and bitemarks were digitally photographed following ABFO guidelines. A semi-automatized technique was used to calculate the inter-canine distance, rotation, eccentricity, angular position, and distance to the arch of each tooth mark. A matrix was created of all possible combinations of dental casts and bitemarks. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of this identification procedure. Among the 1045 comparisons performed, the highest area under the ROC curve (AUC) was obtained for the Euclidean distance of lower teeth rotation (AUC = 0.73). This semi-automatized method to measure lower tooth rotation may be useful to identify individuals responsible for bitemarks and may be applicable in forensic cases.

本研究旨在确定可用于表征人类咬痕与牙列特征的牙科参数，以借助半自动化技术（semi-automatized technology）实现咬痕来源者的身份识别。本次研究共纳入65件牙模（dental casts）与18张咬痕照片。研究人员使用3D激光扫描仪（3D laser scanner）对牙模进行扫描以生成对比叠加图，并按照美国法医牙科学会（ABFO）指南对咬痕进行数字化拍摄。本研究采用半自动化技术计算每处牙痕的犬齿间距、旋转量、偏心率、角位置以及至牙弓的距离。研究人员构建了涵盖所有牙模与咬痕可能组合的矩阵，并通过构建受试者工作特征（Receiver Operating Characteristic，ROC）曲线评估该身份识别流程的准确性。在完成的1045组对比实验中，下颌牙齿旋转的欧氏距离对应的ROC曲线下面积（Area Under the ROC Curve，AUC）最高，达0.73。本研究所用的测量下颌牙齿旋转量的半自动化方法，可用于识别咬痕来源者，亦有望应用于法医鉴定案件中。