Cryoprobe for Endoscopic Enucleation in Children with Pulmonary Tuberculosis: Effective but Not Without Danger - Case Report and Review of Literature.

INTRODUCTION: Tuberculosis (TB) in children under 15 years often results in airway compression, with bronchus intermedius (BI) being the most common site. Endoscopic enucleations can be used to remove lymph nodes and establish an airway in severe cases. Both rigid and flexible bronchoscopy are suitable, with alligator forceps being preferred for its ability to extract tissue. Recent studies have also explored cryoprobe enucleation. CASE PRESENTATION: An HIV-positive boy with persistent symptoms after 9 months of TB treatment was diagnosed based on his mother's and sister's Xpert MTB/RIF positive status. He was started on 4-drug TB treatment, but the child remained clinically symptomatic with abnormal chest X-ray and unconfirmed TB. Bronchoscopy was performed, revealing complete obstruction of BI due to caseating granulomas causing collapse of the right middle and lower lobes. Cryotherapy was used to recanalize the airway, and follow-up bronchoscopy confirmed patent BI. CONCLUSION: While cryotherapy was effective in the restoration of airway patency in this case, there is a lack of knowledge about its use in children.

Detecting missing teeth on PMCT using statistical shape modeling.

The identification of teeth in 3D medical images can be a first step for victim identification from scant remains, for comparison of ante- and postmortem images or for other forensic investigations. We evaluate the performance of a tooth detection approach on mandibles with missing parts or pathologies based on statistical shape models. The proposed approach relies on a shape model that has been built from the full lower jaw, including the mandible and teeth. The model is fitted to the target, resulting in a reconstruction, in addition to a label map that indicates the presence or absence of teeth. We evaluate the accuracy of the proposed solution on a dataset consisting of 76 target mandibles, all extracted from CT images and exhibiting various cases of missing teeth or other cases, such as roots, implants, first dentition, and gap closure. We show an accuracy of approximately 90% on the front teeth (including incisors and canines in our study) that decreases for the molars due to high false-positive rates at the wisdom teeth level. Despite the drop in performance, the proposed approach can be used to obtain an estimate of the tooth count without wisdom teeth, tooth identification, reconstruction of the existing teeth to automate measurements taken as part of routine forensic procedures, or prediction of the missing teeth shape. In comparison to other approaches, our solution relies solely on shape information. This means it can be applied to cases obtained from either medical images or 3D scans because it does not depend on the imaging modality intensities. Another novelty is that the proposed solution avoids heuristics for the separation of teeth or for fitting individual tooth models. The solution is therefore not target-specific and can be directly applied to detect missing parts in other target organs using a shape model of the new target.

Classification of rib fracture types from postmortem computed tomography images using deep learning.

Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images in full detail can be a challenging task. With recent advances in artificial intelligence, an increasing number of systems have been developed to assist clinicians in their work. In this study, the objective was to train a model that can distinguish between various fracture types on different levels of hierarchical taxonomy and detect them on 2D-image representations of volumetric postmortem computed tomography (PMCT) data. We used a deep learning model based on the ResNet50 architecture that was pretrained on ImageNet data, and we used transfer learning to fine-tune it to our specific task. We trained our model to distinguish between "displaced," "nondisplaced," "ad latus," "ad longitudinem cum contractione," and "ad longitudinem cum distractione" fractures. Radiographs with no fractures were correctly predicted in 95-99% of cases. Nondisplaced fractures were correctly predicted in 80-86% of cases. Displaced fractures of the "ad latus" type were correctly predicted in 17-18% of cases. The other two displaced types of fractures, "ad longitudinem cum contractione" and "ad longitudinem cum distractione," were correctly predicted in 70-75% and 64-75% of cases, respectively. The model achieved the best performance when the level of hierarchical taxonomy was high, while it had more difficulties when the level of hierarchical taxonomy was lower. Overall, deep learning techniques constitute a reliable solution for forensic pathologists and medical practitioners seeking to reduce workload.

Towards a New Qualitative Screening Assay for Synthetic Cannabinoids Using Metabolomics and Machine Learning.

BACKGROUND: Synthetic cannabinoids (SCs) are steadily emerging on the drug market. To remain competitive in clinical or forensic toxicology, new screening strategies including high-resolution mass spectrometry (HRMS) are required. Machine learning algorithms can detect and learn chemical signatures in complex datasets and use them as a proxy to predict new samples. We propose a new screening tool based on a SC-specific change of the metabolome and a machine learning algorithm. METHODS: Authentic human urine samples (n = 474), positive or negative for SCs, were used. These samples were measured with an untargeted metabolomics liquid chromatography (LC)-quadrupole time-of-flight-HRMS method. Progenesis QI software was used to preprocess the raw data. Following feature engineering, a random forest (RF) model was optimized in R using a 10-fold cross-validation method and a training set (n = 369). The performance of the model was assessed with a test (n = 50) and a verification (n = 55) set. RESULTS: During RF optimization, 49 features, 200 trees, and 7 variables at each branching node were determined as most predictive. The optimized model accuracy, clinical sensitivity, clinical specificity, positive predictive value, and negative predictive value were 88.1%, 83.0%, 92.7%, 91.3%, and 85.6%, respectively. The test set was predicted with an accuracy of 88.0%, and the verification set provided evidence that the model was able to detect cannabinoid-specific changes in the metabolome. CONCLUSIONS: An RF approach combined with metabolomics enables a novel screening strategy for responding effectively to the challenge of new SCs. Biomarkers identified by this approach may also be integrated in routine screening methods.

Registration based assessment of femoral torsion for rotational osteotomies based on the contralateral anatomy.

BACKGROUND: Computer-assisted techniques for surgical treatment of femoral deformities have become increasingly important. In state-of-the-art 3D deformity assessments, the contralateral side is used as template for correction as it commonly represents normal anatomy. Contributing to this, an iterative closest point (ICP) algorithm is used for registration. However, the anatomical sections of the femur with idiosyncratic features, which allow for a consistent deformity assessment with ICP algorithms being unknown. Furthermore, if there is a side-to-side difference, this is not considered in error quantification. The aim of this study was to analyze the influence and value of the different sections of the femur in 3D assessment of femoral deformities based on the contralateral anatomy. MATERIAL AND METHODS: 3D triangular surface models were created from CT of 100 paired femurs (50 cadavers) without pathological anatomy. The femurs were divided into sections of eponymous anatomy of a predefined percentage of the whole femoral length. A surface registration algorithm was applied to superimpose the ipsilateral on the contralateral side. We evaluated 3D femoral contralateral registration (FCR) errors, defined as difference in 3D rotation of the respective femoral section before and after registration to the contralateral side. To compare this method, we quantified the landmark-based femoral torsion (LB FT). This was defined as the intra-individual difference in overall femoral torsion using with a landmark-based method. RESULTS: Contralateral rotational deviation ranged from 0° to 9.3° of the assessed femoral sections, depending on the section. Among the sections, the FCR error using the proximal diaphyseal area for registration was larger than any other sectional error. A combination of the lesser trochanter and the proximal diaphyseal area showed the smallest error. The LB FT error was significantly larger than any sectional error (p < 0.001). CONCLUSION: We demonstrated that if the contralateral femur is used as reconstruction template, the built-in errors with the registration-based approach are smaller than the intraindividual difference of the femoral torsion between both sides. The errors are depending on the section and their idiosyncratic features used for registration. For rotational osteotomies a combination of the lesser trochanter and the proximal diaphyseal area sections seems to allow for a reconstruction with a minimal error.

RiFNet: Automated rib fracture detection in postmortem computed tomography.

Imaging techniques are widely used for medical diagnostics. In some cases, a lack of medical practitioners who can manually analyze the images can lead to a bottleneck. Consequently, we developed a custom-made convolutional neural network (RiFNet = Rib Fracture Network) that can detect rib fractures in postmortem computed tomography. In a retrospective cohort study, we retrieved PMCT data from 195 postmortem cases with rib fractures from July 2017 to April 2018 from our database. The computed tomography data were prepared using a plugin in the commercial imaging software Syngo.via whereby the rib cage was unfolded on a single-in-plane image reformation. Out of the 195 cases, a total of 585 images were extracted and divided into two groups labeled "with" and "without" fractures. These two groups were subsequently divided into training, validation, and test datasets to assess the performance of RiFNet. In addition, we explored the possibility of applying transfer learning techniques on our dataset by choosing two independent noncommercial off-the-shelf convolutional neural network architectures (ResNet50 V2 and Inception V3) and compared the performances of those two with RiFNet. When using pre-trained convolutional neural networks, we achieved an F1 score of 0.64 with Inception V3 and an F1 score of 0.61 with ResNet50 V2. We obtained an average F1 score of 0.91 ± 0.04 with RiFNet. RiFNet is efficient in detecting rib fractures on postmortem computed tomography. Transfer learning techniques are not necessarily well adapted to make classifications in postmortem computed tomography.

Evaluation of the mediastinal-thoracic volume ratio on postmortem computed tomography.

OBJECTIVES: The aim of this study was to measure the mediastinal-thoracic volume ratio (CTR_VOL) on PMCT as a more accurate version of traditional CTR, in order to assess the terminal positional relationship between the heart and lungs in the different causes of death with regard to age, gender, BMI, cardiomegaly, and lung expansion. MATERIALS: Two hundred fifty consecutive postmortem cases with pre-autopsy PMCT and full forensic autopsy were retrospectively evaluated. The lungs and the mediastinum were manually segmented on the PMCT data and the correspondent volumes were estimated in situ. CTR_VOL was calculated as the ratio of the mediastinal to the thoracic volume. The volume measurements were repeated by the same rater for the evaluation of the intrarater reliability. Age, gender, body weight and height, heart weight at autopsy, and cause of death were retrieved from the autopsy reports. Presence of lung expansion was radiologically evaluated in situ. RESULTS: CTR_VOL was positively associated with age and BMI but not with gender and was higher for cardiomegaly compared to normal hearts, lower for asphyxiation-related deaths compared to cardiac deaths and intoxications, and lower for cases with lung expansion. The intrarater reliability was excellent for the calculated volumes of both lungs and mediastinum. CONCLUSION: The results of the present study support CTR_VOL as a tool to assess the relationship between the heart and lungs in situ, which differs significantly between the studied cause of death categories.

A review of visualization techniques of post-mortem computed tomography data for forensic death investigations.

Postmortem computed tomography (PMCT) is a standard image modality used in forensic death investigations. Case- and audience-specific visualizations are vital for identifying relevant findings and communicating them appropriately. Different data types and visualization methods exist in 2D and 3D, and all of these types have specific applications. 2D visualizations are more suited for the radiological assessment of PMCT data because they allow the depiction of subtle details. 3D visualizations are better suited for creating visualizations for medical laypersons, such as state attorneys, because they maintain the anatomical context. Visualizations can be refined by using additional techniques, such as annotation or layering. Specialized methods such as 3D printing and virtual and augmented reality often require data conversion. The resulting data can also be used to combine PMCT data with other 3D data such as crime scene laser scans to create crime scene reconstructions. Knowledge of these techniques is essential for the successful handling of PMCT data in a forensic setting. In this review, we present an overview of current visualization techniques for PMCT.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.

OBJECTIVES: Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. METHODS: Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. RESULTS: Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. CONCLUSIONS: With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Comparison of superficial wound documentation using 2D forensic photography, 3D photogrammetry, Botscan© and VR with real-life examination.

Evidence acquisition, interpretation and preservation are essential parts of forensic case work that make a standardized documentation process fundamental. The most commonly used method for the documentation and interpretation of superficial wounds is a combination of two modalities: two-dimensional (2D) photography for evidence preservation and real-life examination for wound analysis. As technologies continue to develop, 2D photography is being enhanced with three-dimensional (3D) documentation technology. In our study, we compared the real-life examination of superficial wounds using four different technical documentation and visualization methods.To test the different methods, a mannequin was equipped with several injury stickers, and then the different methods were applied. A total of 42 artificial injury stickers were documented in regard to orientation, form, color, size, wound borders, wound corners and suspected mechanism of injury for the injury mechanism. As the gold standard, superficial wounds were visually examined by two board-certified forensic pathologists directly on the mannequin. These results were compared to an examination using standard 2D forensic photography; 2D photography using the multicamera system Botscan©, which included predefined viewing positions all around the body; and 3D photogrammetric reconstruction based on images visualized both on screen and in a virtual reality (VR) using a head-mounted display (HMD).The results of the gold standard examination showed that the two forensic pathologists had an inter-reader agreement ranging from 69% for the orientation and 11% for the size of the wounds. A substantial portion of the direct visual documentation showed only a partial overlap, especially for the items of size and color, thereby prohibiting the statistical comparison of these two items. A forest plot analysis of the remaining six items showed no significant difference between the methods. We found that among the forensic pathologists, there was high variability regarding the vocabulary used for the description of wound morphology, which complicated the exact comparison of the two documentations of the same wound.There were no significant differences for any of the four methods compared to the gold standard, thereby challenging the role of real-life examination and 2D photography as the most reliable documentation approaches. Further studies with real injuries are necessary to support our evaluation that technical examination methods involving multicamera systems and 3D visualization for whole-body examination might be a valid alternative in future forensic documentation.

An algorithm for automatically generating gas, bone and foreign body visualizations from postmortem computed tomography data.

Post mortem computed tomography (PMCT) can aid in localizing foreign bodies, bone fractures, and gas accumulations. The visualization of these findings play an important role in the communication of radiological findings. In this article, we present an algorithm for automated visualization of gas distributions on PMCT image data of the thorax and abdomen. The algorithm uses a combination of region growing segmentation and layering of different visualization methods to automatically generate overview images that depict radiopaque foreign bodies, bones and gas distributions in one image. The presented method was tested on 955 PMCT scans of the thorax and abdomen. The algorithm managed to generate useful images for all cases, visualizing foreign bodies as well as gas distribution. The most interesting cases are presented in this article. While this type of visualization cannot replace a real radiological analysis of the image data, it can provide a quick overview for briefings and image reports.

Correlation of age, sex and season with the state of human decomposition as quantified by postmortem computed tomography.

Dead bodies exhibit a variable range of changes with advancing decomposition. To quantify intracorporeal gas, the radiological alteration index (RAI) has been implemented in the assessment of postmortem whole-body computed tomography. We used this RAI as a proxy for the state of decomposition. This study aimed to (I) investigate the correlation between the state of decomposition and the season in which the body was discovered; and (II) evaluate the correlations between sociodemographic factors (age, sex) and the state of decomposition, by using the RAI as a proxy for the extent of decomposition. In a retrospective study, we analyzed demographic data from all autopsy reports from the Institute of Forensic Medicine of Zurich between January 2017 to July 2019 and evaluated the radiological alteration index from postmortem whole-body computed tomography for each case. The bodies of older males showed the highest RAI. Seasonal effects had no significant influence on the RAI in our urban study population with bodies mostly being discovered indoors. Autopsy reports contain valuable data that allow interpretation for reasons beyond forensic purposes, such as sociopolitical observations.

Postmortem imaging findings and cause of death determination compared with autopsy: a systematic review of diagnostic test accuracy and meta-analysis.

BACKGROUND: The aim of this study was to evaluate the sensitivity of postmortem computed tomography (PMCT), postmortem magnetic resonance imaging (PMMR) and PMCT angiography (PMCTA) compared with autopsy in cases of adult death investigations. METHODS: For this systematic review and meta-analysis, Embase, PubMed, Scopus, Web of Science and Medline were searched for eligible studies in October 2016; a follow-up literature search was conducted in March 2018. Studies referring to PMCT, PMCTA and/or PMMR of more than 3 cases with subsequent autopsy were included. Data were extracted from published texts in duplicate. The extracted outcomes were categorized as follows: soft tissue and organ findings, skeletal injuries, haemorrhages, abnormal gas accumulations and causes of death. The summary measure was sensitivity, if 3 or more studies were available. To combine studies, a random effects model was used. Variability and heterogeneity within the meta-analysis was assessed. RESULTS: Of 1053 studies, 66 were eligible, encompassing a total of 4213 individuals. For soft tissue and organ findings, there was a high pooled sensitivity with PMCTA (0.91, 95% CI 0.81-0.96), without evidence for between-study variability (Cochrane's Q test p = 0.331, I2 = 24.5%). The pooled sensitivity of PMCT+PMMR was very high in skeletal injuries (0.97, CI 0.87-0.99), without evidence for variability (p = 0.857, I2 = 0.0%). In detecting haemorrhages, the pooled sensitivity for PMCT+PMMR was the highest (0.88, 95% CI 0.35-0.99), with strong evidence of heterogeneity (p < 0.05, I2 > 50%). Pooled sensitivity for the correct cause of death was the highest for PMCTA with 0.79 (95% CI 0.52-0.93), again with evidence of heterogeneity (p = 0.062, I2 > 50%). CONCLUSION: Distinct postmortem imaging modalities can achieve high sensitivities for detecting various findings and causes of death. This knowledge should lead to a reasoned use of each modality. Both forensic evidence and in-hospital medical quality would be enhanced.

Communicating 3D data-interactive 3D PDF documents for expert reports and scientific publications in the field of forensic medicine.

INTRODUCTION: Modern forensic investigations increasingly revert to 3D imaging techniques, such as computed tomography, magnetic resonance imaging, and 3D surface imaging. Findings are therefore often based on 3D data sets; however, this information is commonly reported and communicated within 2D imagery. The use of interactive 3D PDFs is already established in the scientific community but has yet to be implemented in the field of forensic medicine. METHODS AND MATERIALS: Three example cases were chosen to serve as exemplary data for the most commonly applied imaging techniques in postmortem imaging. 3D surface models were created from postmortem magnetic resonance imaging (PMMR), postmortem computed tomography (PMCT), and 3D surface imaging data sets. RESULTS: PMMR revealed a space-occupying subdural hemorrhage that led to ipsilateral compression of the brain tissue of the right hemisphere. PMCT displayed a defect in the skull on the left side of the temporal bone. 3D surface imaging data displayed a patterned discoloration on the inside of the left forearm. DISCUSSION: Interactive 3D PDFs offer the possibility to communicate 3D information to the reader while maintaining all the benefits of a regular 2D PDF. With Adobe Acrobat, the reader can interactively navigate through 3D data sets and create sufficient depth cues to generate a realistic 3D perception of the data. CONCLUSION: The interactive 3D PDF is a useful extension of standard 2D PDFs and has the potential to communicate 3D data to the reader in a more complete, more comprehensible, and less subjective manner than 2D PDFs.

Clinical forensic height measurements on injured people using a multi camera device for 3D documentation.

Documenting the existence, size, position and shape of injuries is an important part of medical forensic examinations. In the photography of an injury, the documentation is limited to an approximation of size and position of the injury based on a ruler included in the image. The documentation of injuries can be improved with photogrammetry, which allows the creation of scaled 3D models of an injury that can be used to not only document and visualize the injury but also to match the injury with an injury-causing object. In this paper, the multicamera device "Botscan" was used to perform 3D whole-body documentation and measure the positions of injuries. A major advantage of 3D whole-body documentation compared to photography is that the former can be performed at a later stage of the investigation. This makes the whole-body 3D documentation of injuries an important tool for re-examination.

Potential use of deep learning techniques for postmortem imaging.

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

CT-based sex estimation on human femora using statistical shape modeling.

OBJECTIVES: Estimating the sex of decomposed corpses and skeletal remains of unknown individuals is one of the first steps in the identification process in forensic contexts. Although various studies have considered the femur for sex estimation, the focus has primarily been on a specific single or a handful of measurements rather than the entire shape of the bone. In this article, we use statistical shape modeling (SSM) for sex estimation. We hypothesize that the accuracy of sex estimation will be improved by using the entire shape. MATERIALS AND METHODS: For this study, we acquired a total of 61 femora from routine postmortem CT scans at the Institute for Forensic Medicine of the University of Zurich. The femora were extracted using segmentation technique. After building a SSM, we used the linear regression and nonlinear support vector machine technique for classification. RESULTS: Using linear logistic regression and only the first principal component of the SSM, 76% of the femora were correctly classified by sex. Using the first five principal components, this value could be increased to 80%. Using nonlinear support vector machines and the first 20 principal components increased the rate of correctly classified femora to 87%. DISCUSSION: Despite some limitations, the results obtained by using SSM for sex estimation in femur were promising and confirm the findings of other studies. Sex estimation accuracy, however, is not significantly improved over single or multiple linear measurements. Further research might improve the sex determination process in forensic anthropology by using SSM.

Applying virtual reality in forensics - a virtual scene walkthrough.

A major task of forensic investigations is the documentation and interpretation of evidence to reconstruct a forensically relevant incident. To accomplish this task, a scene is documented not only with photographs but also with 3D documentation technologies. The resulting 3D data are used for 3D visualization and to perform 3D reconstructions. In this article, we present an approach for using forensic 3D data in conjunction with virtual reality to perform scene walkthroughs in the context of witness or suspect interrogations. The aim is to provide a method for scene visits showing the original scene even years after the incident. These scene walkthroughs in VR can be reproduced and allow to see through the eyes of a witness by recording their behavior and actions. These recordings allow subsequent examinations and reconstruction to support the investigation and scene understanding and can be used as evidence in court.

Application of Cinematic Rendering in Clinical Routine CT Examination of Ankle Sprains.

OBJECTIVE: Cinematic rendering technique is used to generate almost photorealistic 3D reconstructions of volumetric data. The purpose of this study was to evaluate the feasibility of cinematically rendered reconstructions in routine CT examinations of ankle sprains. CONCLUSION: Cinematic rendering technique may be primarily used to deliver visual information to patients, physicians, and virtual anatomy classes. Postprocessing requires more time than traditional methods do, which can be a hindrance in clinical work.

Differentiation of dental restorative materials combining energy-dispersive X-ray fluorescence spectroscopy and post-mortem CT.

Today, post-mortem computed tomography (CT) is routinely used for forensic identification. Mobile energy-dispersive X-ray fluorescence (EDXRF) spectroscopy of a dentition is a method of identification that has the potential to be easier and cheaper than CT, although it cannot be used with every dentition. In challenging cases, combining both techniques could facilitate the process of identification and prove to be advantageous over chemical analyses. Nine dental restorative material brands were analyzed using EDXRF spectroscopy. Their differentiability was assessed by comparing each material's x-ray fluorescence spectrum and then comparing the spectra to previous research investigating differentiability in CT. To verify EDXRF's precision and accuracy, select dental specimens underwent comparative electron beam excited x-ray spectroscopy (EDS) scans, while the impact of the restorative surface area was studied by scanning a row of dental specimens with varying restorative surface areas (n = 10). EDXRF was able to differentiate all 36 possible pairs of dental filling materials; however, dual-energy CT was only able to differentiate 33 out of 36. The EDS scans showed correlating x-ray fluorescence peaks on the x-ray spectra compared to our EDXRF. In addition, the surface area showed no influence on the differentiability of the dental filling materials. EDXRF has the potential to facilitate corpse identification by differentiating and comparing restorative materials, providing more information compared to post-mortem CT alone. Despite not being able to explicitly identify a brand without a control sample or database, its fast and mobile use could accelerate daily routines or mass victim identification processes. To achieve this goal, further development of EDXRF scanners for this application and further studies evaluating the method within a specific routine need to be performed.