Purpose: This study aimed to determine whether structured reports (SRs) reduce reporting time and/or increase the level of detail for trauma CT scans compared to free-text reports (FTRs).
Method: Eight radiology residents used SRs and FTRs to describe 14 whole-body CT scans of patients with polytrauma in a simulated emergency room setting. Each resident created both a brief report and a detailed report for each case using one of the two formats. We measured the time to complete the detailed reports and established a scoring system to objectively measure report completeness and the level of detail. Scoring sheets divided the CT findings into main and secondary criteria. Finally, the radiological residents completed a questionnaire on their opinions of the SRs and FTRs.
Results: The detailed SRs were completed significantly faster than the detailed FTRs (mean 19 min vs. 25 min; p < 0.001). The maximum allowance of 25 min was used for 25% of SRs and 59% of FTRs. For brief reports, the SRs contained more secondary criteria than the FTRs (p = 0.001), but no significant differences were detected in main criteria. Study participants rated their own SRs as significantly more time-efficient, concise, and clearly structured compared to the FTRs. However, SRs and FTRs were rated similarly for quality, accuracy, and completeness.
Conclusion: We found that SRs for whole-body trauma CT add clinical value compared to FTRs because SRs reduce reporting time and increase the level of detail for trauma CT scans.
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http://dx.doi.org/10.1016/j.ejrad.2021.109954 | DOI Listing |
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
Background And Objectives: Recent advances in multimodal large language models (MLLMs) have shown promise in medical image interpretation, yet their utility in surgical contexts remains unexplored. This study evaluates six MLLMs' performance in interpreting diverse imaging modalities for laryngeal cancer surgery.
Methods: We analyzed 169 images (X-rays, CT scans, laryngoscopy, and pathology findings) from 50 patients using six state-of-the-art MLLMs.
3D Print Med
January 2025
Department of Pediatric Cardiology, The Heart Institute, University of Colorado, Children's Hospital Colorado, 13123 E 16th Ave B100, 80045, Aurora, CO, USA.
Background: Despite advancements in imaging technologies, including CT scans and MRI, these modalities may still fail to capture intricate details of congenital heart defects accurately. Virtual 3D models have revolutionized the field of pediatric interventional cardiology by providing clinicians with tangible representations of complex anatomical structures. We examined the feasibility and accuracy of utilizing an automated, Artificial Intelligence (AI) driven, cloud-based platform for virtual 3D visualization of complex congenital heart disease obtained from 3D rotational angiography DICOM images.
View Article and Find Full Text PDFClin Chem Lab Med
January 2025
School of Dentistry and Medical Science, Faculty of Science and Health, 110481 Charles Sturt University, Wagga Wagga, NSW, Australia.
This scoping review focuses on the evolution of pre-analytical errors (PAEs) in medical laboratories, a critical area with significant implications for patient care, healthcare costs, hospital length of stay, and operational efficiency. The Covidence Review tool was used to formulate the keywords, and then a comprehensive literature search was performed using several databases, importing the search results directly into Covidence (n=379). Title, abstract screening, duplicate removal, and full-text screening were done.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Peking University Third Hospital, Beijing, China.
Background: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
Purpose: To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.
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