Grading Abdominal Trauma: Changes in and Implications of the Revised 2018 AAST-OIS for the Spleen, Liver, and Kidney.

According to the Centers for Disease Control and Prevention, trauma is the leading cause of fatal injuries for Americans aged 1-44 years old and the fourth leading overall cause of death. Accurate and early diagnosis, including grading of solid organ injuries after blunt abdominal trauma (BAT), is crucial to guide management and improve outcomes. The American Association for the Surgery of Trauma (AAST) Organ Injury Scale (OIS) is the most widely accepted BAT scoring system at CT both within the United States and internationally, and its uses include stratification of injury severity, thereby guiding management, and facilitation of clinical research, billing, and coding. Furthermore, this system also plays a role in the credentialing process for trauma centers in the United States. The newly revised 2018 OIS provides criteria for grading solid organ damage into three groups: imaging, operation, and pathology. The final grade is based on the highest of the three criteria. If multiple lower-grade (I or II) injuries are present in a single organ, one grade is advanced to grade III. The most substantial change in the revised 2018 AAST-OIS is incorporation of multidetector CT findings of vascular injury, including pseudoaneurysm and arteriovenous fistula. The authors outline the main revised aspects of grading organ injury using the AAST-OIS for the spleen, liver, and kidney after BAT, particularly the role of multidetector CT and alternative imaging in organ injury detection, the importance of vascular injuries in grade change, and the impact of these changes on patient management and in prediction of operative treatment success and in-hospital mortality. ©RSNA, 2023 Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.

Application of artificial intelligence centric workflows for evaluation of neuroradiology emergencies.

The goal of this study was to perform a pilot study to assess user-interface of radiologists with an artificial-intelligence (AI) centric workflow for detection of intracranial hemorrhage (ICH) and cervical spine fractures (CSFX). Over 12-month period, interaction and usage of AI software implemented in our institution, Aidoc, on head and cervical spine CT scans were obtained. Several interaction variables were defined, assessing different types of interaction between readers of different training level and AI software. The median usage of AI-centric workflow for detection of ICH and CSFX were 28.8% and 21.8%, respectively, demonstrating a significant additional engagement beyond Native workflow (worklist and PACS). Further studies are warranted to expand interaction assessments to further understand the value unlocked by the AI-centric workflows.