Cuellar Maria, Gao Sheng, Hofmann Heike
Department of Criminology, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA, 19104, United States.
Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Walnut Street, Philadelphia, PA, 19104, United States.
Forensic Sci Int Synerg. 2024 Jul 24;9:100543. doi: 10.1016/j.fsisyn.2024.100543. eCollection 2024.
Forensic toolmark analysis traditionally relies on subjective human judgment, leading to inconsistencies and lack of transparency. The multitude of variables, including angles and directions of mark generation, further complicates comparisons. To address this, we first generate a dataset of 3D toolmarks from various angles and directions using consecutively manufactured slotted screwdrivers. By using PAM clustering, we find that there is clustering by tool rather than angle or direction. Using Known Match and Known Non-Match densities, we establish thresholds for classification. Fitting Beta distributions to the densities, we allow for the derivation of likelihood ratios for new toolmark pairs. With a cross-validated sensitivity of 98 % and specificity of 96 %, our approach enhances the reliability of toolmark analysis. This approach is applicable to slotted screwdrivers, and for screwdrivers that are made with a similar production method. With data collection of other tools and factors, it could be applied to compare toolmarks of other types. This empirically trained, open-source solution offers forensic examiners a standardized means to objectively compare toolmarks, potentially decreasing the number of miscarriages of justice in the legal system.
传统上，法医工具痕迹分析依赖于主观的人为判断，这导致了不一致性和缺乏透明度。众多变量，包括痕迹产生的角度和方向，使得比对更加复杂。为了解决这个问题，我们首先使用连续制造的一字螺丝刀从不同角度和方向生成一个3D工具痕迹数据集。通过使用PAM聚类，我们发现聚类是按工具进行的，而不是按角度或方向。使用已知匹配和已知不匹配密度，我们建立了分类阈值。将贝塔分布拟合到密度上，我们可以为新的工具痕迹对推导似然比。我们的方法交叉验证的灵敏度为98%，特异性为96%，提高了工具痕迹分析的可靠性。这种方法适用于一字螺丝刀，以及采用类似生产方法制造的螺丝刀。随着对其他工具和因素的数据收集，它可以应用于比较其他类型的工具痕迹。这种经过实证训练的开源解决方案为法医鉴定人员提供了一种客观比较工具痕迹的标准化方法，有可能减少法律系统中误判的数量。
