Schmidt Verena-Maria, Zelger Philipp, Woess Claudia, Pallua Anton K, Arora Rohit, Degenhart Gerald, Brunner Andrea, Zelger Bettina, Schirmer Michael, Rabl Walter, Pallua Johannes D
Institute of Legal Medicine, Medical University of Innsbruck, Muellerstraße 44, 6020 Innsbruck, Austria.
University Clinic for Hearing, Voice and Speech Disorders, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria.
Biology (Basel). 2022 Jul 25;11(8):1105. doi: 10.3390/biology11081105.
It is challenging to estimate the post-mortem interval (PMI) of skeletal remains within a forensic context. As a result of their interactions with the environment, bones undergo several chemical and physical changes after death. So far, multiple methods have been used to follow up on post-mortem changes. There is, however, no definitive way to estimate the PMI of skeletal remains. This research aimed to propose a methodology capable of estimating the PMI using micro-computed tomography measurements of 104 human skeletal remains with PMIs between one day and 2000 years. The present study indicates that micro-computed tomography could be considered an objective and precise method of PMI evaluation in forensic medicine. The measured parameters show a significant difference regarding the PMI for Cort Porosity p < 0.001, BV/TV p > 0.001, Mean1 p > 0.001 and Mean2 p > 0.005. Using a machine learning approach, the neural network showed an accuracy of 99% for distinguishing between samples with a PMI of less than 100 years and archaeological samples.
在法医背景下，估计骨骼遗骸的死后间隔时间（PMI）具有挑战性。由于骨骼与环境的相互作用，死后骨骼会经历多种化学和物理变化。到目前为止，已经使用了多种方法来追踪死后变化。然而，目前尚无确定的方法来估计骨骼遗骸的PMI。本研究旨在提出一种方法，能够利用104具PMI在1天至2000年之间的人类骨骼遗骸的显微计算机断层扫描测量结果来估计PMI。本研究表明，显微计算机断层扫描可被视为法医PMI评估的一种客观、精确的方法。测量参数显示，对于皮质骨孔隙率p<0.001、骨体积分数p>0.001、平均1 p>0.001和平均2 p>0.005，PMI存在显著差异。使用机器学习方法，神经网络在区分PMI小于100年的样本和考古样本时，准确率达到了99%。
