Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.
Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.
Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024.
Purpose: The AAST Organ Injury Scale is widely adopted for splenic injury severity but suffers from only moderate inter-rater agreement. This work assesses SpleenPro, a prototype interactive explainable artificial intelligence/machine learning (AI/ML) diagnostic aid to support AAST grading, for effects on radiologist dwell time, agreement, clinical utility, and user acceptance.
Methods: Two trauma radiology ad hoc expert panelists independently performed timed AAST grading on 76 admission CT studies with blunt splenic injury, first without AI/ML assistance, and after a 2-month washout period and randomization, with AI/ML assistance.
Background: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.
Purpose: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.
Voriconazole, first commercially approved in 2002 as a second-generation antifungal agent, is commonly used in the immunocompromised setting as both a therapeutic and prophylactic agent. Since 2009, scattered case reports and small case series have detailed a secondary drug-related hyperfluorosis-induced painful periostitis that can occur in the treated patient population. We present a pictorial review of the thoracic imaging manifestations utilizing 3 illustrative cases in the setting of both solid organ and bone marrow transplantation.
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