Background: The focused assessment with sonography in trauma (FAST) examination plays an essential role in diagnosing hemoperitoneum in trauma patients to guide prompt operative management. The FAST examination is highly specific for hemoperitoneum in trauma patients, and has been adopted in nontrauma patients to identify intraperitoneal fluid as a cause of abdominal pain or distension. However, causes of false positive FAST examinations have been described and require prompt recognition to avoid diagnostic uncertainty and inappropriate procedures. Most causes of false positive FAST examinations are due to anatomic mimics such as perinephric fat or seminal vesicles, however, modern ultrasound machines use a variety of postprocessing image enhancement techniques that can also lead to novel false positive artifacts.
Case Report: We report cases where experienced clinicians incorrectly interpreted ultrasound findings caused by a novel mimic of hemoperitoneum: the "lipliner sign." It appears most prominently at the edges of solid organs (such as the liver and the spleen), which is the same location most likely to show free fluid in FAST examination in trauma patients. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Clinicians who take care of trauma patients must be familiar with causes of false positive FAST examinations that could lead to a misdiagnosis of hemoperitoneum.
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http://dx.doi.org/10.1016/j.jemermed.2024.06.013 | DOI Listing |
Eur J Prev Cardiol
January 2025
Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
Aim: Primary prevention of cardiovascular disease (CVD) relies on effective risk stratification to guide interventions. Current models, primarily developed using regression analysis, can lead to inaccurate estimates when applied to external populations. This study evaluates the utility of cluster analysis as an alternative method for developing CVD risk stratification models, comparing its performance with established CVD risk prediction models.
View Article and Find Full Text PDFNeuroradiology
January 2025
Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, Jiangsu, China.
Purpose: We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine clinical settings.
Methods: Two distinct cohorts of head CT angiography (CTA) data were assembled to validate an AI model. Cohort 1, comprising gold-standard consecutive CTA cases, was used in an MRMC study involving six board-certified general radiologists.
Arch Dermatol Res
January 2025
Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Lichen planus is a chronic skin lesion characterized by pruritic violaceous papules, which has a high risk of morbidity. Skin microbiota plays an important role in the maintenance of cutaneous mucosal barrier and human health and immune homeostasis. Studies have shown that skin microbiota may play a role in the pathogenesis of lichen planus, but it is not yet clear.
View Article and Find Full Text PDFJMIR Infodemiology
December 2024
Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Montreal, CA.
Background: Many people seek health-related information online. The significance of reliable information became particularly evident due to the potential dangers of misinformation. Therefore, discerning true and reliable information from false information has become increasingly challenging.
View Article and Find Full Text PDFRadiol Artif Intell
January 2025
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Purpose To evaluate the change in DBT-AI (digital breast tomosynthesis-artificial intelligence) case scores over sequential screens. Materials and Methods This retrospective review included 21,108 female patients (mean age, 58.1 ± [SD] 11.
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