Wilk Leah S, Edelman Gerda J, Aalders Maurice C G
Department of Biomedical Engineering and Physics, Amsterdam UMC Location AMC, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.
Co van Ledden Hulsebosch Center, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands.
R Soc Open Sci. 2022 Jul 27;9(7):220162. doi: 10.1098/rsos.220162. eCollection 2022 Jul.
The postmortem interval (PMI), i.e. the time since death, plays a key role in forensic investigations, as it aids in the reconstruction of the timeline of events. Currently, the standard method for PMI estimation empirically correlates rectal temperatures and PMIs, frequently necessitating subjective correction factors. To address this shortcoming, numerical thermodynamic algorithms have recently been developed, providing rigorous methods to simulate postmortem body temperatures. Comparing these with measured body temperatures then allows non-subjective PMI determination. This approach, however, hinges on knowledge of two thermodynamic input parameters, which are often irretrievable in forensic practice: the ambient temperature prior to discovery of the body and the body temperature at the time of death (perimortem). Here, we overcome this critical limitation by combining numerical thermodynamic modelling with surrogate model-based parameter optimization. This hybrid computational framework predicts the two unknown parameters directly from the measured postmortem body temperatures. Moreover, by substantially reducing computation times (compared with conventional optimization algorithms), this powerful approach is uniquely suited for use directly at the crime scene. Crucially, we validated this method on deceased human bodies and achieved the lowest PMI estimation errors to date (0.18 h ± 0.77 h). Together, these aspects fundamentally expand the applicability of numerical thermodynamic PMI estimation.
死后间隔时间（PMI），即死亡后的时间，在法医调查中起着关键作用，因为它有助于重建事件的时间线。目前，估计PMI的标准方法是将直肠温度与PMI进行经验关联，这通常需要主观校正因子。为了解决这一缺点，最近开发了数值热力学算法，提供了模拟死后体温的严格方法。将这些结果与测量的体温进行比较，就可以进行非主观的PMI测定。然而，这种方法取决于两个热力学输入参数的知识，而这两个参数在法医实践中往往无法获取：尸体被发现之前的环境温度和死亡时的体温（濒死体温）。在这里，我们通过将数值热力学建模与基于代理模型的参数优化相结合，克服了这一关键限制。这种混合计算框架直接从测量的死后体温预测这两个未知参数。此外，通过大幅减少计算时间（与传统优化算法相比），这种强大的方法特别适合直接在犯罪现场使用。至关重要的是，我们在尸体上验证了这种方法，并取得了迄今为止最低的PMI估计误差（0.18小时±0.77小时）。总之，这些方面从根本上扩展了数值热力学PMI估计的适用性。
