Forensic Centre for Digital Scanning and 3D Printing, WMG, University of Warwick, Coventry, CV4 7AL, UK.
Forensic Centre for Digital Scanning and 3D Printing, WMG, University of Warwick, Coventry, CV4 7AL, UK.
Forensic Sci Int. 2021 Jan;318:110602. doi: 10.1016/j.forsciint.2020.110602. Epub 2020 Nov 18.
False start analysis is the examination of incomplete saw marks created on bone in an effort to establish information on the saw that created them. The present study aims to use quantitative data from micro-CT cross-sections to predict the thickness of the saw blade used to create the mark. Random forest statistical models are utilised for prediction to present a methodology that is useful to both forensic researchers and practitioners.
340 false starts were created on 32 fleshed cadaveric leg bones by 38 saws of various classes. False starts were micro-CT scanned and seven measurements taken digitally. A regression random forest model was produced from the measurement data of all saws to predict the saw blade thickness from false starts with an unknown class. A further model was created, consisting of three random forests, to predict the saw blade thickness when the class of the saw is known. The predictive capability of the models was tested using a second sample of data, consisting of measurements taken from a further 17 false starts created randomly selected saws from the 38 in the experiment.
Random forest models were able to accurately predict up to 100% of saw blade thicknesses for both samples of false starts.
This study demonstrates the applicability of random forest statistical regression models for reliable prediction of saw blade thicknesses from false start data. The methodology proposed enables prediction of saw blade thickness from empirical data and offers a significant step towards reduced subjectivity and database formation in false start analysis. Application of this methodology to false start analysis, with a more complete database, will allow complementary results to current analysis techniques to provide more information on the saw used in dismemberment casework.
假启动分析是检查在骨上产生的不完整锯痕，以确定产生这些锯痕的锯子的信息。本研究旨在利用来自微 CT 横截面的定量数据来预测产生该痕迹的锯片的厚度。随机森林统计模型用于预测，为法医研究人员和从业人员提供了一种有用的方法。
通过 38 种不同类型的锯子在 32 具带肉的尸体腿骨上制造了 340 个假启动。对假启动进行微 CT 扫描，并对 7 个数字进行测量。从所有锯子的测量数据中生成回归随机森林模型，以从具有未知类别的假启动中预测锯片厚度。进一步创建了一个由三个随机森林组成的模型，以在已知锯片类别的情况下预测锯片厚度。使用第二个数据样本测试模型的预测能力，该样本由从实验中 38 个锯子中随机选择的另外 17 个假启动的测量值组成。
随机森林模型能够准确预测两个假启动样本中高达 100%的锯片厚度。
本研究证明了随机森林统计回归模型在从假启动数据可靠预测锯片厚度方面的适用性。所提出的方法使从经验数据预测锯片厚度成为可能，并为减少假启动分析中的主观性和数据库形成迈出了重要一步。将该方法应用于假启动分析，并建立更完整的数据库，将允许与当前分析技术互补的结果，为肢解案件中使用的锯子提供更多信息。
