Typical median effective radiation doses using an anthropomorphic bone fracture phantom for initial radiographic skeletal surveys in the investigation of suspected physical abuse.

BACKGROUND: A series of 31 radiographs is recommended by the Royal College of Radiologists (RCR) when investigating suspected physical abuse (SPA). OBJECTIVE: To determine the radiation dose delivered for skeletal surveys performed for SPA in Victorian radiology departments based on their local protocols. MATERIALS AND METHODS: A 5-year-old paediatric bone fracture phantom was radiographed at five radiology sites using both the RCR recommended protocol and, where applicable, the local departmental SPA protocol. The radiation doses were measured and recorded. These were scaled down to estimate the effective radiation doses for a 2-year-old child at each site and the associated radiation risks estimated. RESULTS: The median effective dose for all radiographic projections in the RCR skeletal survey radiographic series was 0.09 mSv. The estimated risk of radiation-induced cancer and radiation-induced death from cancer for 2-year-old children is classified as "very low," with girls having a higher risk than boys. CONCLUSION: The median effective radiation dose for the RCR skeletal survey for imaging in SPA was 0.09 mSv resulting in a "very low" additional risk of radiation-induced cancer. The authors will now aim to ascertain whether whole-body CT skeletal survey can replace the radiographic series for imaging in SPA while maintaining a comparable radiation dose

Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.

Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ position, structural changes from decomposition, inconsistent body placement in the scanner, and the presence of foreign bodies. Existing ML approaches in clinical imaging can likely be transferred to the forensic setting with careful consideration to account for the increased variability and temporal factors that affect the data used to train these algorithms. Additional steps are required to deal with these issues, by incorporating the possible variability into the training data through data augmentation, or by using atlases as a pre-processing step to account for death-related factors. A key application of ML would be then to highlight anatomical and gross pathological features of interest, or present information to help optimally determine the cause of death. In this review, we highlight results and limitations of applications in clinical medical imaging that use ML to determine key implications for their application in the forensic setting

Semi-supervised labelling of the femur in a whole-body post-mortem CT database using deep learning.

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

Disaster victim identification: Quality management from an odontology perspective

The desired outcome of the victim identification component of a mass fatality event is correct identification of deceased persons in a timely manner allowing legal and social closure for relatives of the victims. Quality Management across all aspects of the Disaster Victim Identification (DVI) structure facilitates this process. Quality Management in forensic odontology is the understanding and implementation of a methodology that ensures collection, collation and preservation of the maximum amount of available dental data and the appropriate interpretation of that data to achieve outcomes to a standard expected by the DVI instructing authority, impacted parties and the forensic odontology specialist community. Managerial pre-event planning responsibility, via an odontology coordinator, includes setting a chain of command, developing and reviewing standard operating procedures (SOP), ensuring use of current scientific methodologies and staff training. During a DVI managerial responsibility includes tailoring SOP to the specific situation, ensuring member accreditation, encouraging inter-disciplinary cooperation and ensuring security of odontology data and work site. Individual responsibilities include the ability to work within a team, accept peer review, and share individual members' skill sets to achieve the best outcome. These responsibilities also include adherence to chain of command and the SOP, maintenance of currency of knowledge and recognition of professional boundaries of expertise. This article highlights issues of Quality Management pertaining particularly to forensic odontology but can also be extrapolated to all DVI actions.A. W. Lake, H. James, J. W. Berket