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 recanalise 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.

Development of A Micro-CT Scanner with Dual-Energy Option and Endovascular Contrast Agent Administration Protocol for Fetal and Neonatal Virtual Autopsy.

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.

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.

Augmented reality in forensics and forensic medicine - Current status and future prospects.

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.

Technical note: Semiautomated targeted postmortem computed tomography angiography of the pulmonary arteries using a robotic system.

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.

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.

Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network.

Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016−01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48−99.38%) and 100.00% (95% CI 96.38−100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904−0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available.

MRI Segmentation of Cervical Muscle Volumes in Survived Strangulation: Is There an Association between Side Differences in Muscle Volume and the Handedness of the Perpetrator? A Retrospective Study.

We evaluate the potential value of magnetic resonance imaging (MRI) in the examination of survivors of manual strangulation. Our hypothesis was that trauma-induced edema of the cervical muscles might lead to a side difference in the muscle volumes, associated with the handedness of the perpetrator. In 50 individuals who survived strangulation, we performed MRI-based segmentation of the cervical muscle volumes. As a control group, the neck MRIs of 10 clinical patients without prior trauma were used. The ratio of the right to left muscle volume was calculated for each muscle group of the control and strangulation groups. Cutoff values for the assumed physiological muscle volume ratios between the right and left sides were identified from our control group. There was no significant difference among the individuals in the pathological muscle volume ratio between right-handed versus both-handed strangulation for the sternocleidomastoid, pretracheal, anterior deep, or trapezoid muscle groups. Only the posterior deep muscle group showed a statistically significant difference in the pathological muscle volume ratio for both-handed strangulations (p = 0.011). Measurement of side differences in cervical muscle volume does not allow for a conclusion concerning the probable handedness of the perpetrator.

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.

Reconstruction of full femora from partial bone fragments for anthropological analyses using statistical shape modeling.

OBJECTIVES: Due to taphonomic processes such as burial, fire, or animal activity, bones are often found incomplete, which can pose problematic for establishing the biological profile of the deceased using anthropological methods. The aim of this study is to test the feasibility of using statistical shape modeling (SSM) to reconstruct full femora from simulated partial femora and determine the accuracy of the reconstruction. Moreover, we assess the accuracy of sex estimation and the degree of stature error added based on the reconstructed femur using different anthropological methods. METHODS: A total of 42 (28 female, 14 female) 3D models of left femora extracted from computed tomography (CT) scans were used. We performed a leave-one-out cross-validation (LOOCV) where 41 bones were used to build the SSM and one bone was used for testing. This bone was cut in 1 cm steps proximally, distally and from both ends up to 10 cm, reconstructed using SSM, and tested using the methods established by Stewart and Purkait (2005), Trotter and Gleser (1952), as well as a method based on SSM. with landmarks being automatically identified. RESULTS: The error induced by reconstructing the femur to the length measurements was low, which translated into useful stature estimations (single sided cuts up to 10 cm: 0.4-1.1%, double sided<2% for cuts shorter than 6 cm). Using Purkaits method for sex estimation on reconstructed bones looked promising as well (single sided: 90.5% when compared to applying Purkaits method on the original bone, double sided 78.6% (10 cm cut) to 97.6% (1-3 cm cuts)) Using SSM for sex classification looked promising as well (single sided cut: 81-85.7%, double sided cut: 59.5-85.3%) CONCLUSION: SSM can be used to reconstruct fragmented femora. These reconstructions can be used for sex and stature estimations, at the cost of lower accuracy. Using SSM might give investigators an additional tool to gain information about the biological profile of a deceased in cases where the fragmentation of a femur does not allow for using other anthropological methods.

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.

Sperm hunting on optical microscope slides for forensic analysis with deep convolutional networks - a feasibility study.

Microscopic sperm detection is an important task in sexual assault cases. In some instances, the samples contain no or only low amounts of semen. Therefore, the biological material is transferred onto a glass slide and needs to be manually scanned using an optical microscope. This work can be very time consuming, especially when no spermatozoa is present. In such a case, the result needs to be validated. In this article we show how convolutional neural networks can perform this task and how they can reduce the scanning time by locating the sperm cells on images taken under the microscope. For this purpose, we trained a VGG19 network and a VGG19 variation with 1942 images, some containing sperm cells and some not.

The forensic holodeck - Recommendations after 8 years of experience for additional equipment to document VR applications.

The forensic holodeck was first introduced in 2013, using the first upcoming commercially available virtual reality gaming headsets to visualize forensic 3D reconstructions. Following the publication of this development virtual reality was introduced in case work in a variety of different ways. After 8 years of using virtual reality in a professional forensic capacity this professional practice report will show, which equipment is necessary in addition to a virtual reality setup. This mostly includes audio-visual and broadcasting technology for complete documentation of the application of virtual reality, but also some other IT equipment, which should be available for as low as 20'000 US$. Guidelines, hints and tips regarding equipment acquisition, setup and use will be provided and discussed.

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.

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.

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.