Musket ball injuries to the head - Experimental CT-study.

A smoothbore musket firing a round ball was the primary weapon of the infantry from the 16th to mid 19th century. Musket ball injuries are thus relatively common when archaeological remains of battlefield victims from that period are studied. Several experimental studies have focused on terminal ballistics of a musket ball. In addition, there is a good supply of historical records directly from the battlefield and military hospitals. Studies and historical records have both concluded that head injuries are among the most lethal types of musket ball damage. In this study we utilized modern day research methods, including Synbone ballistic skull phantoms and computed tomography (CT) imaging, to examine more closely the head injuries and tissue damage caused by a musket ball. We were especially interested to observe how different musket ball velocities and shooting distances would influence bone and soft tissue defects. Our experiments clearly demonstrated that musket ball was a lethal projectile even from a longer distance. Already at low velocities, the musket ball perforated through the skull. Velocity also influenced the appearance of entrance and exit wounds. CT imaging provided us with a three-dimensional view of the wound channel, skull fragments and lead remnants inside the skull phantom. According to our findings, musket ball velocity influenced defect size and cavitation. In addition, velocity influenced the size and distribution of skull fragments and lead remnants in the wound channel. Combining all these aspects could aid us in studies of archaeological musket ball victims. In particular, they could help us to estimate the shooting distance and shed light on the potential course of events in the battlefield.

Observing the fragmentation of two expanding bullet types and a full metal-jacketed bullet with computed tomography-a forensic ballistics case study.

Computed tomography (CT) may have a crucial role in the forensic documentation and analysis of firearm injuries. The aim of this forensic ballistics case study was to explore whether two types of expanding bullets and a full metal-jacketed bullet could be differentiated by inspecting bullet fragments and fragmentation pattern in CT. Three types of .30 caliber bullets (full metal-jacketed Norma Jaktmatch, expanding full-copper Norma Ecostrike, and expanding soft-point Norma Oryx) were test fired from a distance of 5 m to blocks of 10% ballistic gelatine. CT scans of the blocks were obtained with clinical equipment and metal artifact reduction. Radiopaque fragments were identified and fragmentation parameters were obtained from the scans (total number of fragments, maximum diameter of the largest fragment, distance between entrance and the closest fragment, length of the fragment cloud, and maximum diameters of the fragment cloud). The fragmentation patterns were additionally visualized by means of 3D reconstruction. In CT, the bullet types differed in several fragmentation parameters. While the expanding full-copper bullet Ecostrike left behind only a single fragment near the end of the bullet channel, the soft-point Oryx had hundreds of fragments deposited throughout the channel. For both expanding bullets Ecostrike and Oryx, the fragments were clearly smaller than those left behind by the full metal-jacketed Jaktmatch. This was surprising as the full metal-jacketed bullet was expected to remain intact. The fragment cloud of Jaktmatch had similar mediolateral and superoinferior diameters to that of Oryx; however, fragments were deposited in the second half of the gelatine block, and not throughout the block. This case study provides a basis and potential methodology for further experiments. The findings are expected to benefit forensic practitioners with limited background information on gunshot injury cases, for example, those that involve several potential firearms or atypical gunshot wounds. The findings may prove beneficial for both human and wildlife forensics.

The death of King Charles XII of Sweden revisited.

The death of King Charles XII of Sweden has remained as a mystery for more than three centuries. Was he assassinated by his own men or killed by the enemy fire? Charles was killed by a projectile perforating his skull from left to right. In this study, we utilized a Synbone ballistic skull phantom and modern radiological imaging to clarify the factors behind the observed head injuries. We examined whether a musket ball fired from the enemy lines would be the most potential projectile. Our experiments with a leaden 19.5 mm musket ball demonstrated that at velocities of 200 to 250 m/s, it could cause similar type of injuries as observed in the remains of Charles . The radiological imaging supported the theory that the projectile was not a leaden but of some harder metal, as we could detect remnants of lead inside the wound channel unlike in Charles' case. In addition, our experiments showed that a 19.5mm musket ball produces max. 17mm hole into a felt material . The main evidence supporting 19.5 mm projectile size has been a 19-19.5mm bullet hole in a hat that Charles was wearing during his death. Additional experiments with a 25.4 mm steel ball produced approximately 20 mm hole in the felt. As our musket ball experiments also resulted in considerably smaller cranial injuries than those in Charles' case, we can conclude that the deadly projectile wasn't leaden and was more than 19.5 mm in diameter, potentially an iron cartouche ball that was shot from the enemy lines.

Deep learning in forensic shotgun pattern interpretation - A proof-of-concept study.

Little is known about the potential of artificial intelligence in forensic shotgun pattern interpretation. As shooting distance is among the main factors behind shotgun patterning, this proof-of-concept study aimed to explore the potential of neural net architectures to correctly classify shotgun pattern images in terms of shooting distance. The study material comprised a total of 106 shotgun pattern images from two discrete shooting distances (n = 54 images from 10 m and n = 52 images from 17.5 m) recorded on blank white paper. The dataset was used to train, validate and test deep learning algorithms to correctly classify images in terms of shooting distance. The open source AIDeveloper software was used for the deep learning procedure. In this dataset, a TinyResNet-based algorithm reached the highest testing accuracy of 94%. Of the testing set, the algorithm classified all 10 m patterns correctly, and misclassified one 17.5 m pattern. On the basis of these preliminary data, it seems achievable to develop algorithms that would serve as a beneficial tool for forensic investigators when estimating shooting distances from shotgun patterns. In the future, studies with larger and more complex datasets are needed to develop robust and applicable algorithms for forensic shotgun pattern interpretation.

Deep learning in forensic gunshot wound interpretation-a proof-of-concept study.

While the applications of deep learning are considered revolutionary within several medical specialties, forensic applications have been scarce despite the visual nature of the field. For example, a forensic pathologist may benefit from deep learning-based tools in gunshot wound interpretation. This proof-of-concept study aimed to test the hypothesis that trained neural network architectures have potential to predict shooting distance class on the basis of a simple photograph of the gunshot wound. A dataset of 204 gunshot wound images (60 negative controls, 50 contact shots, 49 close-range shots, and 45 distant shots) was constructed on the basis of nineteen piglet carcasses fired with a .22 Long Rifle pistol. The dataset was used to train, validate, and test the ability of neural net architectures to correctly classify images on the basis of shooting distance. Deep learning was performed using the AIDeveloper open-source software. Of the explored neural network architectures, a trained multilayer perceptron based model (MLP_24_16_24) reached the highest testing accuracy of 98%. Of the testing set, the trained model was able to correctly classify all negative controls, contact shots, and close-range shots, whereas one distant shot was misclassified. Our study clearly demonstrated that in the future, forensic pathologists may benefit from deep learning-based tools in gunshot wound interpretation. With these data, we seek to provide an initial impetus for larger-scale research on deep learning approaches in forensic wound interpretation.