Search Results (103)

Background and clinical significance. Penetrating cardiothoracic wounds require prompt treatment in order to decrease mortality and morbidity. Surgical therapy, aimed at bleeding control and removal of damaged tissue, varies widely from the direct suture of parenchymal lacerations to pneumonectomy, which is characterized by high mortality rates. We report our experience with a patient in hemorrhagic shock due to a gunshot wound to the chest, successfully treated by pneumorrhaphy under cardiopulmonary bypass (CPB). Case presentation. A 53-year-old man with a gunshot wound to the chest was admitted to our Emergency Department. A bedside ultrasonography revealed left pleural and pericardial effusion. He was hemodynamically instable, so he was immediately transferred to the operating room by the cardiac and Thoracic Surgery teams. Through a median sternotomy approximately 2 L of blood were evacuated and a deep laceration of the left upper lobe was discovered. The massive bleeding could not be controlled, leading to pleural cavity flooding. The surgical team decided to institute emergency CPB and perform lung repair by pneumorrhaphy, under circulatory support. The patient survived and was discharged on p.o. day 20. Conclusions. Clinical expertise, adequate instrumental equipment and a high level of interdisciplinary team-work favorably affected the patient’s outcome. Full article

Gunshot injuries are challenging conditions because of the unique characteristics of the wounding agents producing soft tissue damage that may be compounded by the formation of an expanding temporary cavity (cavitation). Variations in ballistic performance leading to higher energy transfer by the projectile, including bullet tumbling, deformation, and fragmentation, cause increased soft tissue injury and may also lead to more extensive bone comminution compromising local blood supply. Once life-threatening injuries have been excluded or properly addressed, the emergency management of localized trauma from bullets and shotgun pellets may be complicated due to progressive tissue necrosis within the zone of injury. Additionally, the risk of infection should be tackled, especially in high energy bone injuries. War experience suggests a baseline separation between wounds with limited tissue destruction which can routinely be managed as simple penetrating injuries and those resulting from high energy transfer to the tissues involving a substantial amount of necrotic elements surrounding the wound channel which call for a more aggressive surgical approach. A further justification for such a distinction is the need for antibiotic therapy, which varies according to most studies depending on the wounding mechanism, the nature of the wound, and the extent of tissue injury. The emergency physician should also be aware of the possibility of “bizarre” bullet paths resulting in occult injuries of important anatomic structures. Full article

This study addresses the detection of gunshot residue (GSR) around a bullet hole, which is one of the key forensic procedures for estimating the firing distance. GSR was inspected using flash-pulse thermography (FPT) with Kurtosis statistical processing. The result of such an inspection is a pattern composed of numerous small indications distributed around the hole, attributed to gunshot residue particles. The number and spatial distribution of these indications depend on the firing distance. Analyzing such results based on individual indications is impractical, as the pattern must be evaluated as a whole. Therefore, quantifying the overall result can significantly improve the analysis of the firing distance estimation. This study presents a quantification procedure based on threshold-based mass-marking of indications and evaluation of several statistical characteristics. The correlation between these characteristics and firing distance is then analyzed. A strong but distinctly nonlinear correlation was found between the firing distance and some simple quantitative characteristics, such as the total number of indications. However, the study shows that some derived characteristics, such as the contrast between marked areas and background, exhibit a near-linear correlation. These parameters are, therefore, promising for firing distance analysis based on FPT inspection of GSR on through-shot targets. Full article

As tropical forests become increasingly vulnerable to land use changes, fragmentation, and climate shifts, efforts to minimise species loss are essential. Prevalent in most environments and having complex vocalisations, birds are key indicators of ecosystem health and a good model for acoustic monitoring. In Brazil, the Caparaó National Park (CNP) is a preserved remnant of the Atlantic Forest with great avian endemism. Despite having >600 species, limited research has utilised bioacoustics for species assessment. This study employed bioacoustics to examine soundscapes and community composition at two CNP locations—one with ombrophilous montane forest (OMF) and another with semi-deciduous seasonal forest (SSF). Four SongMeters were deployed, recording bird choruses from 08:00 to 11:00 a.m. for two months. Soundscape profiles and species composition were characterised using Raven Pro. Acoustic indices assessed correlations with avian species richness, and sites were compared using the Mann–Whitney U test. Ninety-eight species were detected, and species richness was greater within SSF. While acoustic indices had little impact on richness, they identified differing soundscapes: more ambient noise in OMF, and gunshots detected in SSF. The results indicate that bioacoustics can aid monitoring strategies. Given the presence of rare species and illegal activities, more studies are needed to support the conservation of birds in this critical environment. Full article

Objective: Traumatic anterior skull base fractures can be associated with significant morbidity and are managed based on the method of injury, presence of cerebrospinal leak, clinical stability and other factors. Our objective is to determine factors associated with the development of infection in the management of surgical anterior skull base fractures. Methods: We completed a retrospective review of adult patients with traumatic anterior skull base fractures treated operatively with bi-frontal craniotomy from 2012 to 2022. The statistical analysis was completed with Prism 9.0 software for spearman correlation coefficient analysis. Results: Our study had a total of 51 patients. The average time from arrival to surgery was four days. A total of 20 patients were injured via gunshot wound (GSW) and 31 via other methods of injury. A total of 81% of patients were started on antibiotics prior to surgery, and all patients were kept on antibiotics after surgery. Five patients developed infections. Infection was not correlated with time to surgery, length of stay, type/duration of antibiotics, EVD placement/duration, or lumbar drain placement. However, all but one of the infections were in patients injured by GSWs vs. other mechanisms of injury (p < 0.01), and duration, not merely presence, of lumbar drain was correlated with development of infection (p < 0.01). Conclusions: In the treatment of anterior skull base fractures, time to surgery does not significantly increase risk of infection. However, patients with gunshot wounds have higher risks of infection, and thus more aggressive management should be considered. Full article

Gunshot sound classification plays a crucial role in public safety, forensic investigations, and intelligent surveillance systems. This study evaluates the performance of deep learning models in classifying firearm sounds by analyzing twelve time–frequency spectrogram representations, including Mel, Bark, MFCC, CQT, Cochleagram, STFT, FFT, Reassigned, Chroma, Spectral Contrast, and Wavelet. The dataset consists of 2148 gunshot recordings from four firearm types, collected in a semi-controlled outdoor environment under multi-orientation conditions. To leverage advanced computer vision techniques, all spectrograms were converted into RGB images using perceptually informed colormaps. This enabled the application of image processing approaches and fine-tuning of pre-trained Convolutional Neural Networks (CNNs) originally developed for natural image classification. Six CNN architectures—ResNet18, ResNet50, ResNet101, GoogLeNet, Inception-v3, and InceptionResNetV2—were trained on these spectrogram images. Experimental results indicate that CQT, Cochleagram, and Mel spectrograms consistently achieved high classification accuracy, exceeding 94% when paired with deep CNNs such as ResNet101 and InceptionResNetV2. These findings demonstrate that transforming time–frequency features into RGB images not only facilitates the use of image-based processing but also allows deep models to capture rich spectral–temporal patterns, providing a robust framework for accurate firearm sound classification. Full article

Background/Objectives: The integration of artificial intelligence (AI) into forensic science is expanding, yet its application in firearm injury diagnostics remains underexplored. This study investigates the diagnostic capabilities of ChatGPT-4 (February 2024 update) in classifying gunshot wounds, specifically distinguishing entrance from exit wounds, and evaluates its potential, limitations, and forensic applicability. Methods: ChatGPT-4 was tested using three datasets: (1) 36 firearm injury images from an external database, (2) 40 images of intact skin from the forensic archive of the University of Catania (negative control), and (3) 40 real-case firearm injury images from the same archive. The AI’s performance was assessed before and after machine learning (ML) training, with classification accuracy evaluated through descriptive and inferential statistics. Results: ChatGPT-4 demonstrated a statistically significant improvement in identifying entrance wounds post-ML training, with enhanced descriptive accuracy of morphological features. However, its performance in classifying exit wounds remained limited, reflecting challenges noted in forensic literature. The AI showed high accuracy (95%) in distinguishing intact skin from injuries in the negative control analysis. A lack of standardized datasets and contextual forensic information contributed to misclassification, particularly for exit wounds. Conclusions: While ChatGPT-4 is not yet a substitute for specialized forensic deep learning models, its iterative learning capacity and descriptive improvements suggest potential as a supplementary diagnostic tool in forensic pathology. However, risks such as overconfident misclassifications and AI-generated hallucinations highlight the need for expert oversight and cautious integration in forensic workflows. Future research should prioritize dataset expansion, contextual data integration, and standardized validation protocols to enhance AI reliability in medico-legal diagnostics. Full article

Abdominal arteriovenous fistula [AVF] is a rare but serious complication of penetrating trauma, often associated with high morbidity and mortality. This report presents the case of a 24-year-old male who sustained multiple gunshot wounds, leading to the formation of an ilio-iliac AVF and a pseudoaneurysm. The patient arrived at the emergency department hemodynamically unstable, with bullet wounds to the forearm, thigh, and lumbosacral region. Initial non-arterial phase CT revealed a pseudoaneurysm anterior to the right external iliac vessels and a surrounding hematoma, raising suspicion for AVF. A second biphasic CTA confirmed an AVF connection between the right external iliac artery and external iliac vein, as well as the arterialization of the vein. Additionally, fat stranding and bowel wall thickening suggested potential hollow viscus injury. Due to the patient’s unstable condition and possible intra-abdominal injuries, an open laparotomy was performed. A stent was placed in the right external iliac artery, the vein was primarily repaired, and serosal injuries to the duodenum and cecum were surgically addressed. The patient recovered gradually, although a persistent serous discharge was noted and managed in follow-up. This case highlights the importance of considering AVF in penetrating abdominal trauma and the critical role of biphasic CTA in diagnosis and surgical planning. Full article

Background/Objectives: Artificial intelligence (AI) is beginning to be applied in wound ballistics, showing preliminary potential to improve the accuracy and objectivity of forensic analyses. This review explores the current state of AI applications in forensic firearm wound analysis, emphasizing its potential to address challenges such as subjective interpretations and data heterogeneity. Methods: A systematic review adhering to PRISMA guidelines was conducted using databases such as Scopus and Web of Science. Keywords focused on AI and GSW classification identified 502 studies, narrowed down to 4 relevant articles after rigorous screening based on inclusion and exclusion criteria. Results: These studies examined the role of deep learning (DL) models in classifying GSWs by type, shooting distance, and entry or exit characteristics. The key findings demonstrated that DL models like TinyResNet, ResNet152, and ConvNext Tiny achieved accuracy ranging from 87.99% to 98%. Models were effective in tasks such as classifying GSWs and estimating shooting distances. However, most studies were exploratory in nature, with small sample sizes and, in some cases, reliance on animal models, which limits generalizability to real-world forensic scenarios. Conclusions: Comparisons with other forensic AI applications revealed that large, diverse datasets significantly enhance model performance. Transparent and interpretable AI systems utilizing techniques are essential for judicial acceptance and ethical compliance. Despite the encouraging results, the field remains in an early stage of development. Limitations highlight the need for standardized protocols, cross-institutional collaboration, and the integration of multimodal data for robust forensic AI systems. Future research should focus on overcoming current data and validation constraints, ensuring the ethical use of human forensic data, and developing AI tools that are scientifically sound and legally defensible. Full article

Background/Objectives: Recent breakthroughs in three-dimensional (3D) printing and high-resolution imaging have opened up new possibilities in personalized medicine, surgical planning, and forensic reconstruction. This study breaks new ground by evaluating the integration of high-resolution peripheral quantitative computed tomography (HR-pQCT) with multimodal imaging and additive manufacturing to assess a chronic, infected gunshot injury in the knee joint of a red deer. This unique approach serves as a translational model for complex skeletal trauma. Methods: Multimodal imaging—including clinical CT, MRI, and HR-pQCT—was used to characterise the extent of osseous and soft tissue damage. Histopathological and molecular analyses were performed to confirm the infectious agent. HR-pQCT datasets were segmented and processed for 3D printing using PolyJet, stereolithography (SLA), and fused deposition modelling (FDM). Printed models were quantitatively benchmarked through 3D surface deviation analysis. Results: Imaging revealed comminuted fractures, cortical and trabecular degradation, and soft tissue involvement, consistent with chronic osteomyelitis. Sphingomonas sp., a bacterium that forms biofilms, was identified as the pathogen. Among the printing methods, PolyJet and SLA demonstrated the highest anatomical accuracy, whereas FDM exhibited greater geometric deviation. Conclusions: HR-pQCT-guided 3D printing provides a powerful tool for the anatomical visualisation and quantitative assessment of complex bone pathology. This approach not only enhances diagnostic precision but also supports applications in surgical rehearsal and forensic analysis. It illustrates the potential of digital imaging and additive manufacturing to advance orthopaedic and trauma care, inspiring future research and applications in the field. Full article

This case report describes index pancreatoduodenectomy in a 32-year-old male following a close-range gunshot wound to the abdomen, with consequent 4 cm pancreatic head defect, duodenal and common bile duct perforation, right kidney laceration, and through-and-through inferior vena cava (IVC) injury. Although standard trauma protocols often favor damage control surgery (DCS) with delayed reconstruction in unstable patients, this patient’s hemodynamic stability—attributed to retroperitoneal self-tamponade—enabled a single-stage definitive approach. The rationale for immediate reconstruction was to prevent the risks associated with delayed management, such as ongoing pancreatic and biliary leakage, chemical peritonitis, and subsequent sepsis or hemorrhage. This case highlights that, in select stable patients with severe pancreaticoduodenal trauma, immediate pancreatoduodenectomy may be preferable to DCS, provided care is delivered in a high-volume hepatopancreaticobiliary (HPB) center with appropriate expertise and resources. Full article

Background: Surgical care in conflict regions like Tigray, Ethiopia, faces severe challenges due to limited resources, infrastructural deficiencies, and high trauma burden. From 2019 to 2023, a multidisciplinary team conducted five humanitarian missions focusing on orthoplastic reconstruction of extremity and mandibular injuries from high-energy gunshot trauma. Methods: A retrospective analysis was performed on 98 patients who underwent free or pedicled flap reconstruction. Data included demographics, flap type, technique, complications, follow-up, and early clinical outcomes score as well as mobility scores. Flaps were harvested using loupes anastomosis performed using microscopes, depending on availability. Results: Among 98 patients (25.5% female, 74.5% male), 69 free flaps and 38 pedicled flaps were performed. Free fibula flaps (n = 54) included 33 mandibular and 21 extremity reconstructions. Additional flaps included ALT, gracilis, and LD flaps. Pedicled flaps included 18 fibula and 20 ALT/LD flaps. Mean age was 35.5 years; mean operative time was 429.5 min, with mandibular fibula transfers being longest. Microsurgical techniques were used in 34% of cases. Median follow-up was 10 months. Microsurgical complications occurred in 18.4%, mainly in fibula transfers (25.9%). Non-microsurgical issues included wound infections (n = 15), graft loss (n = 3), and bleeding (n = 5). Flap loss occurred in 16.3% overall. Early clinical outcome results were good (30.6%), acceptable (28.6%), and moderate (24.5%). Conclusions: Orthoplastic reconstruction using both free and pedicled flaps is feasible in low-resource, conflict settings. Despite infrastructural challenges, functional outcomes were achievable, supporting the value of adaptable microsurgical strategies in humanitarian surgery. Full article

Deep learning has emerged as a powerful technique for speech enhancement, particularly in security systems where audio signals are often degraded by non-stationary noise. Traditional signal processing methods struggle in such conditions, making it difficult to detect critical sounds like gunshots, alarms, and unauthorized speech. This study investigates a hybrid deep learning framework that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) to enhance speech quality and improve sound classification accuracy in noisy security environments. The proposed model is trained and validated using real-world datasets containing diverse noise distortions, including VoxCeleb for benchmarking speech enhancement and UrbanSound8K and ESC-50 for sound classification. Performance is evaluated using industry-standard metrics such as Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and Signal-to-Noise Ratio (SNR). The architecture includes multi-layered neural networks, residual connections, and dropout regularization to ensure robustness and generalizability. Additionally, the paper addresses key challenges in deploying deep learning models for security applications, such as computational complexity, latency, and vulnerability to adversarial attacks. Experimental results demonstrate that the proposed DNN + GAN-based approach significantly improves speech intelligibility and classification performance in high-interference scenarios, offering a scalable solution for enhancing the reliability of audio-based security systems. Full article

Deep learning has brought substantial progress to medical imaging, which has resulted in continuous improvements in diagnostic procedures. Through deep learning architecture implementations, radiology professionals achieve automated pathological condition detection, segmentation, and classification with improved accuracy. The research tackles a rarely studied clinical medical imaging issue that involves bullet identification and positioning within X-ray images. The purpose is to construct a sturdy deep learning system that will identify and classify ballistic trauma in images. Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. Research data was developed by replicating controlled bullet damage in chest X-rays while expanding to a wider range of anatomical areas that include the legs, abdomen, and head. Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. Multiple computational systems were used to verify the results, which showcased the effectiveness of the proposed solution. This research provides new perspectives on understanding forensic radiology trauma assessment by developing the first deep learning system that detects and classifies gun-related radiographic injuries automatically. The first system for forensic radiology designed with automated deep learning to classify gunshot wounds in radiographs is introduced by this research. This approach offers new ways to look at trauma which is helpful for work in clinics as well as in law enforcement. Full article

The decision to remove a bullet from a gunshot victim depends on its location and associated medical risks, with surgical extraction often not indicated. Radiological imaging plays a vital role in assessing gunshot wounds and locating bullets, and it is essential in both clinical and forensic contexts. This narrative review examines the use of computed tomography (CT) for virtual bullet analysis, providing insights into shape, design, fragmentation, and material composition. Traditional 2D X-ray imaging, though commonly used, has limitations in accurately assessing caliber and position due to magnification and its 2D nature. In contrast, CT scans generate 3D reconstructions for detailed and precise examination, overcoming challenges such as metal artifacts with techniques such as extended Hounsfield unit (HU) reconstructions. These methods enhance the visualization of metal objects, allowing for better analyses of lodged bullets. Dual-energy CT further differentiates materials, such as lead and copper, using HU value differences at two energy levels. These advancements enable the virtual classification, shape analysis, and material identification of bullets in forensic investigations, even when the bullet remains in the body. As CT technology progresses, its forensic applications are expected to improve, providing more accurate and comprehensive differentiations of bullet types in future cases. Full article