Purpose Computed tomography (CT) has transformed medical diagnostics by providing detailed two- and three-dimensional images of internal structures. However, interpreting CT data remains challenging and time-consuming due to the need for repeated image evaluations in various window settings. Adjusting window and level parameters not only consumes time but also complicates the analysis of pathologies involving multiple tissue types. Method This study introduces a novel AI-driven multi-tissue windowing (M-Win) technique for CT data visualization. The approach consists of two main steps: automatic segmentation using a convolutional neural network based on the U-Net architecture, followed by mapping the segmented structures to their respective window settings. The U-Net model was trained and validated on a dataset of postmortem CT scans covering the chest, abdomen, and pelvis. The algorithm assigns each voxel to a specific window based on standard Hounsfield unit (HU) ranges, translating HU values within a window to corresponding grayscale levels. Values below the window range appear black, while those above appear white, enabling simultaneous visualization of multiple tissue windows within a single image. Discussion M-Win holds promise for enhancing CT applications across various medical fields. Compared to existing techniques, the U-Net-based method offers advantages such as reduced window border artifacts. Although current limitations include segmentation artifacts and processing time, the potential benefits of streamlined and comprehensive CT analysis make M-Win a valuable addition to medical imaging. Future research will focus on clinical integration, real-time windowing capabilities, and performance comparisons with standard and alternative image review methods.

This paper aims to determine if photogrammetry and/or structured light scanning (SLS) can be used as alternatives for conducting metric assessments on mandibles as opposed to computed tomography (CT). Identifying alternative imaging options to CT is important as, although CT is accurate and reliable, there are multiple limitations that impact its accessibility, including cost, portability, and ease of learning. Photogrammetry and SLS are two handheld, lightweight, cost-effective alternatives that are relatively user friendly. Therefore, this paper imaged 33 human mandibles using CT, photogrammetry, and SLS. For intra-observer error, the ICC results were between 0.37 and 1, indicating a range of unreliable to excellent measurements. Almost all TEM results were within the acceptable threshold set for this study (< 2). Despite graphical demonstrations of the measurements being provided to the observers, all inter-observer results were unreliable, indicating that further refinement of these definitions is required before a protocol can be developed. However, all inter-modality assessments were within the acceptable threshold (< 5 TEM). The range for the ICC results for CT v photogrammetry was between 0.37 and 0.88. For CT v SLS, the results were between 0.47 and 0.87. Overall, photogrammetry had the highest degree of consistency with CT, but all methods proved to be interchangeable. This paper highlights that alternative imaging options are available in scenarios where CT cannot be accessed.

Purpose: 2D fast Fourier transforms (FFT) and wavelet transforms (WT) are two mathematical techniques to analyze signals and decompose them into different frequency components. These two techniques have been used in various manners to denoise images and enhance feature extraction. In this study, we show how small and imbalanced datasets encoded with 2D fast Fourier and wavelet transforms can substantially reduce the search time during model selection. We demonstrate our approach on postmortem computed tomography (PMCT) image data that has been preprocessed to visualize gas distribution and show how this technique enhances model selection to predict the Radiological Alteration Index (RAI) value.Methods: An original set of PMCT image data were classified by a set of model architecture, pre-selected for evaluation. For architecture screening, each candidate model was trained in a base configuration under a shared hyperparameter grid; two multi-domain feature extraction variants per base model included either a 2D FFT or WT channel, encoded by a lightweight ResNet18 encoder, and concatenated with the original image encoder —ResNet18 or Vision Transformer (ViT). Performance and stability were assessed by mean and standard deviation of validation accuracy and F1 across repeated runs.Results: Our ViT architecture performed better than ResNet18 with a maximum accuracy of 0.9412 for the binary classification (RAI ≤ 50, RAI > 50) and 0.8353 for the four-fold classification.Conclusion: Encoding image datasets with 2D FFT and WT and using this transformation as an additional modality, helps to stabilize hyperparameter optimization, reducing temporal costs during architecture selection.

Purpose: Computed tomography (CT) has become a widely adopted and standard procedure as an adjunct to autopsies in numerous countries. However, owing to the high number of cases and the limited availability of skilled practitioners, the need to streamline the diagnostic process has spurred the advancement of automated solutions. These solutions leverage deep learning methodologies to potentially automate diagnoses by analyzing postmortem CT data. Here, we show how deep learning techniques enable segmentation and volume evaluation to be concurrently performed for six basic thoracic and abdominal organs in postmortem CT data: the heart, lungs, liver, spleen, kidneys, and urinary bladder. Based on these automated volumetric estimations we automatically derived the weight of the heart, lungs, liver, spleen, and kidneys. Methods: We developed a convolutional neural network tailored for conducting volumetric data segmentation in postmortem computed tomography images based on the U-Net architecture. Results: Our best model achieved an overall Dice score (F1 score) of 0.907 +/- 0.029. The heart, lung, and liver yielded higher scores than did the spleen, kidneys, and urinary bladder. We also automated the weight calculation of the heart, lungs, liver, spleen, and kidneys. Conclusion: Our study demonstrated that a convolution neural network such as U-Net could reliably estimate concurrently the volumes of six basic thoracic and abdominal organs from postmortem CT data. Our study also shows how this information can be subsequently used to automatically estimate their weight. However, post-and perimortem changes pose substantial challenges for automatically processing postmortem CT data.

When the time since death must be calculated forensic pathologists often consider a calculation based on the Henssge nomogram. This calculation requires an estimated body weight. Previous research has indicated that healthcare workers generally inaccurately guessed patients' body weights. In recent years, weight estimation methods based on anthropometric parameters, such as mid-arm or waist circumference, have been shown to improve estimation accuracy. This study aimed to examine whether anthropometric weight estimation methods could improve weight estimation accuracy compared to visual estimation in forensic pathology. In 199 cases from a Swiss population, we measured the actual body weight, mid-arm circumference, waist circumference, and body height before autopsy. Additionally, two forensic pathologists visually estimated the body weight. We found mid-arm circumferences to correlate the strongest with actual body weight (Pearson 'sr 0.87, 95 % CI 0.83-0.90). However, all mid-arm circumference-based estimation methods performed worse than those previously described. A statistical bias between -12.3 % and -14.5 % indicated a systematic weight underestimation. Combined two-physician visual estimation performed significantly better than anthropometric measurements in our population but showed no difference from anthropometric estimation methods previously described in the literature. Further research is needed on novel body weight estimation methods that are currently not applicable for the global population.

Background and objective Suicide has a profound impact on both the affected families and society at large. Among young adults it even ranks as the fourth leading cause of death. Therefore, analysis of suicides is crucial for enhancing prevention strategies. This study aims to (I) investigate sex and age differences, (II) differences in methods and (III) locations (urban vs. rural) among those who committed suicide over a time period of 10 years in the catchment area of the Institute of Forensic Medicine, University of Zurich. Material and methods The archive of the Institute of Forensic Medicine, University of Zurich was searched for postmortem examinations and autopsy reports from completed suicides over a time period of 10 years. All relevant data were extracted from the written reports and five age groups were defined (group I ≤ 30 years, group II 31–44 years, group III 45–54 years, group IV 55–64 years and group V > 64 years). Nonparametric Kruskal-Wallis one-way variance analysis by rank was used for the statistical analysis on each criterion. Results Of the 1174 individuals included in the study, 72% were male, and 28% were female, with a mean age of approximately 52 years at the time of suicide. No relevant change was observed in the male-to-female ratio over the 10 years; however, women showed a trend toward a lower age at suicide. In terms of suicide methods, men had a higher rate of shooting (21.2% vs. 3.6%, p < 0.1) and hanging (24.4% vs. 16.4%, p < 0.1), whereas women had a higher rate of intoxication (21.6% vs. 9.0%, p < 0.1). The choice of suicide method also varied across age groups. Regarding location, completed suicides declined in urban regions but increased in rural regions. Conclusion Prevention plans should be reviewed, especially given the trend toward younger women completing suicide. Suicide prevention remains a major sociopolitical challenge that demands continuous review and the adaptation of suicide prevention strategies.

Purpose: To assess (I) whether, in autopsy-proven lethal intoxications with opiates/opioids, a dilatation of the common bile duct (CBD) is still visible in postmortem computed tomography (PMCT) and (II) if a dilatation of the CBD might also be measurable for other substance groups (e.g., stimulants, hypnotics, antipsychotics, etc.). Methods: We retrospectively measured the CBD using PMCT in cases with lethal intoxication (n = 125) and as a control group in cases with a negative toxicological analysis (n = 88). Intoxicating substances were classified into the subgroups (opiates, opioids, stimulants, hypnotics, antipsychotics, gasses, and others). Significance between the study and control groups was tested with the Mann–Whitney U test, and correlations were examined by using crosstables. Results: There was a statistically significant difference between the CBD diameters in the intoxication group overall, when compared to the CBD diameter in the control group (p < 0.001). For both subgroups of “opiates” and “opioids”, there was a strong statistically significant difference between the CBD diameter (being wider) in those groups compared to the control group (both p = 0.001). For the three subgroups “hypnotics”, “stimulants”, and “psychotropic drugs”, there was no statistically significant difference between the CBD diameters in the intoxication subgroups when compared with the control group. The other subgroups were too small for statistical analysis. Conclusion: A dilated common bile duct in postmortem computed tomography might be used as an indication for a lethal opioid or opiate intoxication only in regard to the specific case circumstances or together with other indicative findings in a postmortem investigation.

Conventional photogrammetry faces challenges with non-textured, transparent, or reflective surfaces, affecting accurate 3D modeling, particularly in forensic documentation. This study evaluates improvements using lower exposure, exposure bracketing, and RAW format for better 3D modeling of such surfaces. Two bodies were photographed under controlled conditions to assess techniques for non-textured surfaces, with a comparison set for textured surfaces. The experiments were conducted in an autopsy room with a Nikon D5500 camera, adjusting for low exposure, exposure bracketing, RAW format, and increased photo redundancy. Models with Meshroom (Alicevision). Our focus was on visual plausibility rather than quantitative metrics. Results indicated that using RAW format with exposure bracketing and low exposure significantly improved 3D models by reducing artificial edges seen with standard JPG images, despite some noise. A redundant series of RAW photos further reduced artifacts and noise, demonstrating the effectiveness of averaging photos to enhance model quality. However, these modifications showed marginal improvements on textured surfaces, underscoring their significant benefits primarily for non-textured surfaces. This study highlights the potential of modified photogrammetry techniques in forensic science, particularly for documenting challenging surfaces. It points out the need for further research, given its limitations in sample size and the absence of extensive parameter testing and quantitative analysis.

Objectives Photogrammetry is widely used in forensic practice to create 3D models of crime scenes, bodies, living individuals, and objects. However, it has limitations in accurately capturing transparent, reflective, and low-texture surfaces, which can hinder forensic investigations. Neural Radiance Fields (NeRFs), a recently developed method, offer a potential solution by creating more accurate and detailed 3D models in these challenging contexts. This study aims to evaluate whether NeRFs can serve as effective alternatives to structure-from-motion (SfM) photogrammetry for recording forensic autopsies. Materials and Methods Photogrammetric scans were performed on a variety of forensic subjects, including a cadaver with skin discoloration and epidermal exfoliation, a metal trashcan, a vehicle, and a mock crime scene. The scans were processed using traditional photogrammetry software (Meshroom) and compared with NeRF-based visualizations generate using instant neural graphics primitives. Results NeRF-based models provided more lifelike and detailed visualizations than photogrammetry, particularly when documenting transparent, reflective, or featureless surfaces. NeRF demonstrated superior capability in capturing complex details that photogrammetry struggled with. Conclusion NeRF technology shows considerable promise for improving the documentation of forensic autopsies, offering enhanced visual fidelity for challenging surfaces such as transparent or reflective materials. While the method presents challenges related to editing, software compatibility, and high computational demands, its potential benefits in forensic investigations are evident and merit further exploration.

The changes that occur to the human body after death reflect a multitude of complex biological processes, which can be impacted by a collection of variables that are not yet fully understood. Typically, information is obtained through in-situ examination and/or 2D data collection, which may restrict the availability of data and prevent collection of valuable information. To address this gap, the aim of this paper is to present a protocol for 3D data collection of human decomposition in outdoor environments. The specific objectives include presenting an approach and framework using wildlife cameras and performing 3D observation of a decomposing body. The method includes the design and construction of a walk-in cage including five frames holding 31 cameras, installed at the Australian Facility for Taphonomic Experimental Research. Preliminary trials completed on one subject in Zurich and various objects provided promising results through the generation of a 3D model. Comparing the wildlife cameras 3D model with a high-quality 3D model showed only minor discrepancies. This approach will be used in a study designed to improve our understanding of the human decomposition process to ultimately assist investigators with PMI estimations and to help reconstruct the sequence of events and time of death.

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.

The rate of parental consent for fetal and perinatal autopsy is decreasing, whereas parents are more likely to agree to virtual autopsy by non-invasive imaging methods. Fetal and perinatal virtual autopsy needs high-resolution and good soft-tissue contrast for investigation of the cause of death and underlying trauma or pathology in fetuses and stillborn infants. This is offered by micro-computed tomography (CT), as opposed to the limited resolution provided by clinical CT scanners, and this is one of the most promising tools for non-invasive perinatal postmortem imaging. We developed and optimized a micro-CT scanner with a dual-energy imaging option. It is dedicated to post-mortem CT angiography and virtual autopsy of fetuses and stillborn infants in that the chamber can be cooled down to around 5 °C; this increases tissue rigidity and slows decomposition of the native specimen. This, together with the dedicated gantry-based architecture, attempts to reduce potential motion artifacts. The developed methodology is based on prior endovascular injection of a BaSO4-based contrast agent. We explain the design choices and considerations for this scanner prototype. We give details of the treatment of the optimization of the dual-energy and virtual mono-energetic imaging option that has been based on minimizing noise propagation and maximizing the contrast-to-noise ratio for vascular features. We demonstrate the scanner capabilities with proof-of-concept experiments on phantoms and stillborn piglets.

Background and objective Suicide has a profound impact on both the affected families and society at large. Among young adults it even ranks as the fourth leading cause of death. Therefore, analysis of suicides is crucial for enhancing prevention strategies. This study aims to (I) investigate sex and age differences, (II) differences in methods and (III) locations (urban vs. rural) among those who committed suicide over a time period of 10 years in the catchment area of the Institute of Forensic Medicine, University of Zurich.Material and methods The archive of the Institute of Forensic Medicine, University of Zurich was searched for postmortem examinations and autopsy reports from completed suicides over a time period of 10 years. All relevant data were extracted from the written reports and five age groups were defined (group I <= 30 years, group II 31-44 years, group III 45-54 years, group IV 55-64 years and group V > 64 years). Nonparametric Kruskal-Wallis one-way variance analysis by rank was used for the statistical analysis on each criterion.Results Of the 1174 individuals included in the study, 72% were male, and 28% were female, with a mean age of approximately 52 years at the time of suicide. No relevant change was observed in the male-to-female ratio over the 10 years; however, women showed a trend toward a lower age at suicide. In terms of suicide methods, men had a higher rate of shooting (21.2% vs. 3.6%, p < 0.1) and hanging (24.4% vs. 16.4%, p < 0.1), whereas women had a higher rate of intoxication (21.6% vs. 9.0%, p < 0.1). The choice of suicide method also varied across age groups. Regarding location, completed suicides declined in urban regions but increased in rural regions.Conclusion Prevention plans should be reviewed, especially given the trend toward younger women completing suicide. Suicide prevention remains a major sociopolitical challenge that demands continuous review and the adaptation of suicide prevention strategies.

Department of Paediatrics and Child Health, Stellenbosch University and Tygerberg Hospital, Faculty of Medicine and Health Sciences, Cape Town, South Africa Division of Forensic Medicine, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University and Forensic Pathology Service, Tygerberg, Cape Town, South Africa 3D Center Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland Department of Pediatric Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

Purpose. - To (I) evaluate the reproducibility of a urinary bladder volume cut off of > 330 ml as an indicator of lethal intoxication and to (II) investigate the correlation of urinary bladder volume with lethal intoxications for different substance subgroups. Materials and methods. - We investigated a postmortem study group of individuals with lethal intoxication (n = 180) versus a no intoxication control group (n = 102 individuals). Urinary bladder volume was taken from the autopsy report. Based on receiver operating characteristic curve (ROC) analysis, we evaluated both lethal intoxication in general and lethal intoxication in the opioid, cocaine, benzodiazepine, psychotropic drug, amphetamine and z-drug subgroups. Results. - Urinary bladder volume was significantly higher in individuals with lethal intoxication in general (median volume 130 ml) than in the control group (median volume 8 ml). In ROC analysis, bladder volume exhibited good discriminatory performance (AUC 0.71). Sensitivity and specificity were maximized at a cutoff point of 190 ml (45%/88%). For the opioid, cocaine, benzodiazepine and amphetamine subgroups as the leading substances, a distended urinary bladder could be confirmed as a possible sign of lethal intoxication. We found no bladder distention in the cases of lethal intoxication with psychotropic drugs and z-drugs as the leading substances.

Forensic investigations require a vast variety of knowledge and expertise of each specialist involved. With the increase in digitization and advanced technical possibilities, the traditional use of a computer with a screen for visualization and a mouse and keyboard for interactions has limitations, especially when visualizing the content in relation to the real world. Augmented reality (AR) can be used in such instances to support investigators in various tasks at the scene as well as later in the investigation process. In this article, we present current applications of AR in forensics and forensic medicine, the technological basics of AR, and the advantages that AR brings for forensic investigations. Furthermore, we will have a brief look at other fields of application and at future developments of AR in forensics.

INTRODUCTION:To better depict vascular lesions on postmortem computed tomography (PMCT), whole-body postmortem computed tomography angiography (PMCTA) can be used in forensic diagnostics. Targeted angiography, in which only a specific vessel is filled with contrast agent, might help in cases of traumatic changes that render whole-body PMCTA impossible. Moreover, in targeted PMCTA, the contrast agent does not affect the haptics of any other organs. In this article, we describe automated, CT-guided targeted angiography of the pulmonary artery (PA) using the Virtobot system. MATERIAL AND METHODS:Our study group consisted of 8 deceased persons (3 males, 5 females). We first performed an unenhanced CT scan and used the data obtained to plan the needle trajectories with the Virtobot planning software. Then, the needle was fully automatically placed by the Virtobot system. Subsequently, 50 ml of contrast agent was injected manually, and the CT scan was repeated (targeted PMCTA). RESULTS AND DISCUSSION:We tested a new method for performing semiautomated targeted postmortem angiography of the PAs using a robotic needle placement system (Virtobot). In 6 out of our 8 cases, the injection of contrast agent in the PA was successful. In five of the six successful cases, there was reflux of contrast agent to some extent, but the reflux did not affect the readout. In general, the procedure was easy to plan based on a PMCT data set, and the pulmonary trunk was easy to reach with a robotic needle placement system.

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.