Download full-text PDF

Source

Publication Analysis

Top Keywords

radiological cases
4
cases month
4
month systemic
4
systemic air
4
air embolism
4
embolism vein
4
vein galen
4
galen malformation
4
radiological
1
month
1

Similar Publications

Background: Helicobacter pylori bacteria colonize the gastric mucosa and contribute to the occurrence and development of gastrointestinal diseases. According to the WHO, H. pylori bacteria are considered class I carcinogen.

View Article and Find Full Text PDF

Background: Ductal carcinoma in situ (DCIS) is overtreated, in part because of inability to predict which DCIS cases diagnosed at core needle biopsy (CNB) will be upstaged at excision. This study aimed to determine whether quantitative magnetic resonance imaging (MRI) features can identify DCIS at risk of upstaging to invasive cancer.

Methods: This prospective observational clinical trial analyzed women with a diagnosis of DCIS on CNB.

View Article and Find Full Text PDF

Introduction: The anterior division of the internal iliac artery (ADIIA) is a crucial vascular structure that supplies blood to the pelvic organs, perineum, and gluteal region. The present study demonstrates practical data concerning the anatomy of the ADIIA and its branches. It is hoped that the results of the current study may aid in localizing the pelvic arteries effectively.

View Article and Find Full Text PDF

Purpose: To determine whether there is a difference in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values in white matter pathways in the subacute period after COVID-19 infection and to evaluate the correlation between diffusion tensor imaging (DTI) metrics and laboratory findings.

Material And Methods: The study included 64 healthy controls and 91 patients. Patients were classified as group 1 (all patients, n = 91), group 2 (outpatients, n = 58), or group 3 (inpatients, n = 33).

View Article and Find Full Text PDF

Open-Source Large Language Models in Radiology: A Review and Tutorial for Practical Research and Clinical Deployment.

Radiology

January 2025

From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201 (C.H.S., A.K., V.P., F.X.D.); Departments of Radiology, Medicine, and Biomedical Data Science, Stanford University, Palo Alto, Calif (C.P.L.); Department of Computer Science and Electrical Engineering, College of Engineering and Information Technology, University of Maryland, Baltimore County, Baltimore, Md (A.J.); Department of Computer Science, University of Maryland, College Park, College Park, Md (H.H.); and University of Maryland Institute for Health Computing, University of Maryland, North Bethesda, Md (H.H., F.X.D.).

Integrating large language models (LLMs) into health care holds substantial potential to enhance clinical workflows and care delivery. However, LLMs also pose serious risks if integration is not thoughtfully executed, with complex challenges spanning accuracy, accessibility, privacy, and regulation. Proprietary commercial LLMs (eg, GPT-4 [OpenAI], Claude 3 Sonnet and Claude 3 Opus [Anthropic], Gemini [Google]) have received much attention from researchers in the medical domain, including radiology.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!