Search Results (3)

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

Background: Firearm wounds tend to have a precise pattern. Despite this, real-world case presentations can present uncertain elements, sometimes deviating from what is considered standard, and present uncommon features that are difficult for forensic pathologists and ballistic experts to explain. Methods: A retrospective analysis of autopsy reports from the Institute of Legal Medicine, University of Catania, covering 2019–2023, included 348 judicial inspections and 378 autopsies performed as part of the institute’s overall activities. Among these, seventeen cases of firearm deaths were identified, with three atypical cases selected for detailed analysis. An interdisciplinary approach involving forensic pathology, radiology, and ballistics was used. Results: The selected cases included: (1) A 56-year-old female with a thoracic gunshot wound involving three 7.65 caliber bullets, displaying complex trajectories and retained bullets; (2) A 48-year-old male with two cranial gunshot injuries, where initial evaluation suggested homicide staged as a suicide, later confirmed to be a single self-inflicted shot; and (3) A 51-year-old male was found in a car with two gunshot wounds to the head, involving complex forensic evaluation to distinguish between entrance and exit wounds and determine trajectory. The findings showed significant deviations from standard patterns, underscoring the critical role of radiological imaging and ballistic analysis in understanding wound morphology and projectile trajectories. Conclusions: This case series highlights the necessity for standardized yet adaptable protocols and cooperation among forensic specialists. A flexible approach allows forensic investigations to be tailored to the specific circumstances of each case, ensuring that essential examinations are conducted while unnecessary procedures are avoided. Comprehensive data collection from autopsies, gross organ examinations, and, when needed, radiological and histological analysis is essential to accurately diagnose injuries, trace bullet trajectories, retrieve retained projectiles, and determine the fatal wound, particularly in complex cases or those involving multiple shooters. Full article