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Much has been written about bite mark comparisons and bite mark analysis. It has largely been written for, and remained within the domain of, the forensic odontologist despite the limited number of such specialists and the even smaller subset who have expertise in this area. For those health professionals who work in the field of clinical forensic medicine, most will not have access to a forensic odontologist. Courts can be reticent about the costs involved in obtaining another expert opinion. For health professionals, who will likely encounter more bite marks in their career than forensic odontologists, the difficulty is knowing what can legitimately be opined about such injuries., Competing Interests: Declaration of Competing interest There are no declarations of interest., (Crown Copyright © 2024. Published by Elsevier Ltd. All rights reserved.)

A deep learning pipeline was developed and used to localize and classify a variety of implants in the femur contained in whole-body post-mortem computed tomography (PMCT) scans. The results provide a proof-of-principle approach for labelling content not described in medical/autopsy reports. The pipeline, which incorporated residual networks and an autoencoder, was trained and tested using n = 450 full-body PMCT scans. For the localization component, Dice scores of 0.99, 0.96, and 0.98 and mean absolute errors of 3.2, 7.1, and 4.2 mm were obtained in the axial, coronal, and sagittal views, respectively. A regression analysis found the orientation of the implant to the scanner axis and also the relative positioning of extremities to be statistically significant factors. For the classification component, test cases were properly labelled as nail (N + ), hip replacement (H + ), knee replacement (K + ) or without-implant (I - ) with an accuracy >97%. The recall for I - and H + cases was 1.00, but fell to 0.82 and 0.65 for cases with K + and N + . This semi-automatic approach provides a generalized structure for image-based labelling of features, without requiring time-consuming segmentation., Competing Interests: Declaration of competing interest The authors certify that there is no conflict of interest with any financial organization regarding the materials presented in the manuscript., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Reviewing prehospital trauma deaths provides an opportunity to identify system improvements that may reduce trauma mortality. The objective of this study was to identify the number and rate of potentially preventable trauma deaths through expert panel reviews of prehospital and early in-hospital trauma deaths., Methods: We conducted a retrospective review of prehospital and early in-hospital (<24 h) trauma deaths following a traumatic out-of-hospital cardiac arrest that were attended by Ambulance Victoria (AV) in the state of Victoria, Australia, between 2008 and 2014. Expert panels were used to review cases that had resuscitation attempted by paramedics and underwent a full autopsy. Patients with a mechanism of hanging, drowning or those with anatomical injuries deemed to be unsurvivable were excluded., Results: Of the 1183 cases that underwent full autopsies, resuscitation was attempted by paramedics in 336 (28%) cases. Of these, 113 cases (34%) were deemed to have potentially survivable injuries and underwent expert panel review. There were 90 (80%) deaths that were not preventable, 19 (17%) potentially preventable deaths and 4 (3%) preventable deaths. Potentially preventable or preventable deaths represented 20% of those cases that underwent review and 7% of cases that had attempted resuscitation., Conclusions: The number of potentially preventable or preventable trauma deaths in the pre-hospital and early in-hospital resuscitation phase was low. Specific circumstances were identified in which the trauma system could be further improved., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)