The impact of forensic experience on postmortem CT interpretation in firearm deaths: an interobserver reliability...

Forensic radiology training significantly enhances the diagnostic accuracy of postmortem computed tomography (PMCT) in firearm-related deaths, reducing inter-reader variability and improving injury detection. This study examines the impact of forensic expertise on PMCT interpretation, evaluating whether specialized training supersedes clinical radiology experience (non-forensic). A retrospective observational analysis was conducted at the University of Palermo, examining 10 firearm fatalities (homicides or suicides) between 2021 and 2024. The sample included individuals aged 25 to 66, with injuries from both short- and long-barrel firearms. Four radiologists with varying forensic experience analyzed the PMCT scans: an experienced forensic radiologist, an experienced clinical radiologist without forensic training, a radiology resident with forensic training, and a radiology resident without forensic expertise. Findings were compared against autopsy results as the gold standard. A lesion-based analysis was carried out in performance metrics considering the total number of findings (n = 960) and the number of findings in each subgroup (ranging from 40 up to 230 lesions). Inter-rater agreement was assessed using Fleiss' kappa and Cohen's kappa, while diagnostic performance was evaluated with ROC curve analysis. Results showed significantly higher diagnostic accuracy among radiologists with forensic training, particularly in detecting entrance and exit wounds, as well as organ injuries. These findings underscore the critical role of forensic radiology training in enhancing PMCT reliability, particularly for firearm-related injuries. Standardized reporting protocols and structured training programs are crucial for strengthening medicolegal investigations, thereby ensuring accurate and reproducible forensic imaging assessments. Future research should explore advanced imaging techniques, including radiomics and AI-driven analysis, to optimize forensic radiology practices.