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

A study to determine a practical method for weight estimation of deceased persons

BACKGROUND

Body weight is an important parameter for estimating the postmortem interval (PMI) at a crime scene. However, a challenge arises at crime scenes when a weight scale for measuring the total body weight is unavailable. Anthropometry-based models to estimate body weight have been developed in previous studies. This study aims to determine the accuracy of body weight estimations by practitioners, test the anthropometry-based models for applicability to deceased individuals, and examine a potential new method based on applying heel weight.

METHODS

A prospective study was conducted at the Isala Hospital in the Netherlands. During the study period, deceased people that were admitted to the hospital mortuary were included consecutively. The body weight of deceased persons estimated by practitioners was compared to the actual body weight. Anthropometric measurements were taken and used to perform eight sex dependant anthropometry-based models, with accuracy for the actual body weight calculated using RMSE values. A Pearson's correlation test was used to determine the correlation between heel weight and total body weight.

RESULTS

During the study period, a total of 100 cases, 56 males and 44 females, were included. Overall, only 33.3 % of practitioners' estimations were within 5 % of the actual measured weight. The model based on abdominal and thigh circumference performed best for weight estimation in males and the models based on mid-arm circumference, abdominal circumference, calf circumference and, in one model, subscapular skinfold performed best in females. A Pearson's correlation test revealed a weak positive correlation between weight of the heel and total body weight (Pearson's correlation coefficient: 0.214).

DISCUSSION

Estimations of underweight or obese patients posed a challenge for weight estimation. Especially in these cases, study results showed that anthropometry-based models have potential for daily practice. However, additional research is required to assess the reliability of the best performing models before implementation in forensic casework. The correlation between the weight of the heel and body weight was low, therefore implementation of the current method is not recommended, and further research is required.

Handheld hyperspectral imaging as a tool for the post-mortem interval estimation of human skeletal remains

In forensic medicine, estimating human skeletal remains' post-mortem interval (PMI) can be challenging. Following death, bones undergo a series of chemical and physical transformations due to their interactions with the surrounding environment. Post-mortem changes have been assessed using various methods, but estimating the PMI of skeletal remains could still be improved. We propose a new methodology with handheld hyperspectral imaging (HSI) system based on the first results from 104 human skeletal remains with PMIs ranging between 1 day and 2000 years. To differentiate between forensic and archaeological bone material, the Convolutional Neural Network analyzed 65.000 distinct diagnostic spectra: the classification accuracy was 0.58, 0.62, 0.73, 0.81, and 0.98 for PMIs of 0 week-2 weeks, 2 weeks-6 months, 6 months-1 year, 1 year-10 years, and >100 years, respectively. In conclusion, HSI can be used in forensic medicine to distinguish bone materials >100 years old from those <10 years old with an accuracy of 98%. The model has adequate predictive performance, and handheld HSI could serve as a novel approach to objectively and accurately determine the PMI of human skeletal remains.

geoFOR: A collaborative forensic taphonomy database for estimating the postmortem interval

Accurately assessing the postmortem interval (PMI), or the time since death, remains elusive within forensic science research and application. This paper introduces geoFOR, a web-based collaborative application that utilizes ArcGIS and machine learning to deliver improved PMI predictions. The geoFOR application provides a standardized, collaborative forensic taphonomy database that gives practitioners a readily available tool to enter case information that automates the collection of environmental data and delivers a PMI prediction using statistically robust methods. After case submission, the cross-validating machine learning PMI predictive model results in a R² value of 0.82. Contributors receive a predicted PMI with an 80% confidence interval. The geoFOR database currently contains 2529 entries from across the U.S. and includes cases from medicolegal investigations and longitudinal studies from human decomposition facilities. We present the overall findings of the data collected so far and compare results from medicolegal cases and longitudinal studies to highlight previously poorly understood limitations involved in the difficult task of PMI estimation. This novel approach for building a reference dataset of human decomposition is forensically and geographically representative of the realities in which human remains are discovered which allows for continual improvement of PMI estimations as more data is captured. It is our goal that the geoFOR data repository follow the principles of Open Science and be made available to forensic researchers to test, refine, and improve PMI models. Mass collaboration and data sharing can ultimately address enduring issues associated with accurately estimating the PMI within medicolegal death investigations.

PMI estimation through metabolomics and potassium analysis on animal vitreous humour

INTRODUCTION

The estimation of post-mortem interval (PMI) remains a major challenge in forensic science. Most of the proposed approaches lack the reliability required to meet the rigorous forensic standards.

OBJECTIVES

We applied ¹H NMR metabolomics to estimate PMI on ovine vitreous humour comparing the results with the actual scientific gold standard, namely vitreous potassium concentrations.

METHODS

Vitreous humour samples were collected in a time frame ranging from 6 to 86 h after death. Experiments were performed by using ¹H NMR metabolomics and ion capillary analysis. Data were submitted to multivariate statistical data analysis.

RESULTS

A multivariate calibration model was built to estimate PMI based on 47 vitreous humour samples. The model was validated with an independent test set of 24 samples, obtaining a prediction error on the entire range of 6.9 h for PMI < 24 h, 7.4 h for PMI between 24 and 48 h, and 10.3 h for PMI > 48 h. Time-related modifications of the ¹H NMR vitreous metabolomic profile could predict PMI better than potassium up to 48 h after death, whilst a combination of the two is better than the single approach for higher PMI estimation.

CONCLUSION

The present study, although in a proof-of-concept animal model, shows that vitreous metabolomics can be a powerful tool to predict PMI providing a more accurate estimation compared to the widely studied approach based on vitreous potassium concentrations.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat

Background

The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method.

Materials and methods

In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ.

Results

The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle.

Conclusion

Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

Post-Mortem Interval and Microbiome Analysis through 16S rRNA Analysis: A Systematic Review

The determination of the Post-Mortem Interval (PMI) is an issue that has always represented a challenge in the field of forensic science. Different innovative approaches, compared to the more traditional ones, have been tried over the years, without succeeding in being validated as successful methods for PMI estimation. In the last two decades, innovations in sequencing technologies have made it possible to generate large volumes of data, allowing all members of a bacterial community to be sequenced. The aim of this manuscript is to provide a review regarding new advances in PMI estimation through cadaveric microbiota identification using 16S rRNA sequencing, in order to correlate specific microbiome profiles obtained from different body sites to PMI. The systematic review was performed according to PRISMA guidelines. For this purpose, 800 studies were identified through database searching (Pubmed). Articles that dealt with PMI estimation in correlation with microbiome composition and contained data about species, body site of sampling, monitoring time and sequencing method were selected and ultimately a total of 25 studies were considered. The selected studies evaluated the contribution of the various body sites to determine PMI, based on microbiome sequencing, in human and animal models. The results of this systematic review highlighted that studies conducted on both animals and humans yielded results that were promising. In order to fully exploit the potential of the microbiome in the estimation of PMI, it would be desirable to identify standardized body sampling sites and specific sampling methods in order to align data obtained by different research groups.

Application of Micro-Computed Tomography for the Estimation of the Post-Mortem Interval of Human Skeletal Remains

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.

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.