Developing a standardized approach for bite mark identification: introducing a Principal Component Analysis based framework for dental impressions

Forensic odontology plays a crucial role in criminal investigations and victim identification, especially when other evidence is unavailable. Bite mark analysis, a key area within this field, has long been criticized for its lack of standardization and susceptibility to examiner bias. This study addresses these challenges by introducing a standardized methodology that uses Principal Component Analysis (PCA) to compare bite marks using 3D dental molds. By developing a composite scoring system that captures key dental features, such as inter-molar distances, surface areas, and rotational angles, this approach reduces subjective variability, aiming to establish a scientifically rigorous framework for bite mark comparisons. Dental impressions from 30 participants (all aged 16 to maintain age consistency) were analyzed through high-resolution imaging and statistical methods. Key performance metrics include a precision of 0.67, recall of 0.69, specificity of 0.95, accuracy of 0.91, an Area Under the Curve (AUC) of 0.91, and a Precision-Recall Area Under the Curve (PR-AUC) of 0.75. These metrics highlight the model’s ability to balance precision and recall while minimizing both false positives and false negatives—essential requirements in forensic contexts where reliable evidence is critical for accurate legal outcomes. Compared to existing studies in forensic odontology, this model’s precision and AUC are consistent with or exceed those reported in similar quantitative methodologies for bite mark analysis. However, similar to prior research, this study highlights the importance of larger and more diverse datasets to validate and extend its applicability. Expanding demographic diversity and testing across different forensic scenarios will be essential for broader implementation. This research presents an advancement in the standardization of forensic bite mark analysis, laying the groundwork for future studies to refine the model and enhance its reliability. By establishing a data-driven approach, this study contributes toward improving the credibility and reproducibility of forensic evidence.