This prospective study examined the accuracy of ultrasound in diagnosing occult groin hernias in adults. The study included 52 consecutive patients reviewed in the surgical out-patient clinic with a history suggestive of groin hernia but with a normal or inconclusive clinical examination. Each patient underwent a preliminary ultrasound examination by an experienced consultant radiologist who was aware that the patient had a history suggestive of a hernia but was blinded to the side of the symptoms. The patient then proceeded to herniography, and some patients also had surgical exploration. The results of the ultrasound were assessed in relation to the herniography, and the patients who proceeded to surgical exploration had further correlation with surgery. Ultrasound had a sensitivity of 29% and specificity of 90% compared with the herniography. Correlation with surgical findings showed ultrasound to have a sensitivity of 33% and a specificity of 100%. The sensitivity of ultrasound in detecting clinically occult hernias in a non-acute presentation is poor, and patients with normal ultrasound should be considered for further investigation.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00330-005-2825-7 | DOI Listing |
Invest Radiol
January 2025
From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).
Objectives: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Objective: This study aimed to introduce and evaluate a novel software-based system, BioTrace, designed for real-time monitoring of thermal ablation tissue damage during image-guided radiofrequency ablation for hepatocellular carcinoma (HCC).
Methods: BioTrace utilizes a proprietary algorithm to analyze the temporo-spatial behavior of thermal gas bubble activity during ablation, as seen in conventional B-mode ultrasound imaging. Its predictive accuracy was assessed by comparing the ablation zones it predicted with those annotated by radiologists using contrast-enhanced computed tomography (CECT) 24 hours post-treatment, considered the gold standard.
PLoS One
January 2025
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results.
View Article and Find Full Text PDFIntroduction: Diagnosing dementia remains challenging in low-income settings due to limited diagnostic options and the absence of definitive biomarkers. The use of brain MRI in the diagnosis of dementia is infrequent in Uganda, and even when it is used, subtle findings like mild regional atrophy are often overlooked, despite being crucial for imaging diagnosis.
Objective: The purpose of this study was to explore the perceptions and practices of imaging personnel and physicians regarding the use of brain MRI as a diagnostic approach for dementia in Uganda.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!