Amsterdam Research Initiative for Sub-surface Taphonomy and Anthropology (ARISTA) - A taphonomic research facility in the Netherlands for the study of human remains

Morphological changes and protein degradation during the decomposition process of pig cadavers placed outdoors or in tents-a pilot study

The delimitation of the postmortem interval (PMI) is of utmost importance in forensic science. It is especially difficult to determine the PMI in advanced decomposition stages and/or when dead bodies are found under uncommon circumstances, such as tents, or other (semi-) enclosed environments. In such cases, especially when insect access is restricted, morphological assessment of body decomposition is one of the remaining approaches for delimitation of the PMI. However, as this method allows only vague statements/indications about the PMI, it is required to develop new and more reliable methods. One of the most important candidates is the biochemical analysis of protein degradation. In this regard, it has been demonstrated that specific skeletal muscle protein degradation patterns characterize certain time points postmortem and thus can be used as markers for PMI estimation. In order to test this method in different micro-environments, a pilot study using ten pig carcasses was conducted in summer in Northern Germany. The cadavers were openly placed outside (freely accessible for insects), as well as enclosed in tents nearby, and left to decompose to investigate decomposition processes over a time course of 10 days. Muscle samples of the M. biceps femoris were collected on a regular basis and processed via SDS-PAGE and degradation patterns of selected proteins identified by Western blotting. In addition, morphological changes of the cadavers during decomposition were assessed using the total body score (TBS). Results showed that postmortem protein degradation patterns are largely consistent between treatment groups (open field versus tents) despite major morphological differences in the decomposition rate. This field study provides evidence that muscle protein degradation is mostly unaffected by different levels of exposure, making it a sufficient candidate for PMI delimitation under various circumstances.

Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies

BACKGROUND /PURPOSE

Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, both terms are highly associated and sometimes used interchangeably. This systematic review aims to evaluate the application and performance of AI technology for the prediction of PMI.

METHODS

Systematic literature search across different electronic databases using PubMed/Google Scholar/EMBASE/Scopus/CINAHL/Web of Science/Cochrane library was conducted from inception to 3 December 2021 for preclinical and clinical studies reported ML models for PMI estimation.

RESULTS

We identified 18 studies (12 preclinical and 06 clinical) that met the inclusion criteria in the qualitative analysis. Most of the studies employed supervised learning (N = 15), and others employed unsupervised learning (N = 3). Due to the heterogeneity of the samples, quantitative analysis was not performed.

CONCLUSION

In this systematic review, we discussed the performance of AI-based automated systems in PMI estimation. ML models have demonstrated accuracy and precision and the ability to overcome human errors and bias. However, the research is limited, conducted in primarily small, selected human populations. In addition, we suggest further research in larger population-based studies is needed to fully understand the extent of integrated ML models.

Next-generation time of death estimation: combining surrogate model-based parameter optimization and numerical thermodynamics

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.

Individualised and non-contact post-mortem interval determination of human bodies using visible and thermal 3D imaging

Determining the time since death, i.e., post-mortem interval (PMI), often plays a key role in forensic investigations. The current standard PMI-estimation method empirically correlates rectal temperatures and PMIs, frequently necessitating subjective correction factors. To overcome this, we previously developed a thermodynamic finite-difference (TFD) algorithm, providing a rigorous method to simulate post-mortem temperatures of bodies assuming a straight posture. However, in forensic practice, bodies are often found in non-straight postures, potentially limiting applicability of this algorithm in these cases. Here, we develop an individualised approach, enabling PMI reconstruction for bodies in arbitrary postures, by combining photogrammetry and TFD modelling. Utilising thermal photogrammetry, this approach also represents the first non-contact method for PMI reconstruction. The performed lab and crime scene validations reveal PMI reconstruction accuracies of 0.26 h ± 1.38 h for true PMIs between 2 h and 35 h and total procedural durations of ~15 min. Together, these findings broaden the potential applicability of TFD-based PMI reconstruction.

Establishing the time since death (TSD) is vital in many forensic investigations. By combining thermometry, photogrammetry and numerical thermodynamic modelling, the TSD can be determined non-invasively for bodies of arbitrary shape and posture with an unprecedented accuracy of 0.26 h ± 1.38 h.

The applicability of forensic time since death estimation methods for buried bodies in advanced decomposition stages

Estimation of the postmortem interval in advanced postmortem stages is a challenging task. Although there are several approaches available for addressing postmortem changes of a (human) body or its environment (ecologically and/or biochemically), most are restricted to specific timeframes and/or individual and environmental conditions. It is well known, for instance, that buried bodies decompose in a remarkably different manner than on the ground surface. However, data on how established methods for PMI estimation perform under these conditions are scarce. It is important to understand whether and how postmortem changes are affected under burial conditions, if corrective factors could be conceived, or if methods have to be excluded for respective cases. We present the first multi-methodological assessment of human postmortem decomposition carried out on buried body donors in Europe, at the Amsterdam Research Initiative for Sub-surface Taphonomy and Anthropology (ARISTA) in the Netherlands. We used a multidisciplinary approach to investigate postmortem changes of morphology, skeletal muscle protein decomposition, presence of insects and other necrophilous animals as well as microbial communities (i.e., microbiomes) from August to November 2018 associated with two complete body exhumations and eight partial exhumations. Our results clearly display the current possibilities and limitations of methods for PMI estimation in buried remains and provide a baseline for future research and application.