The authors compared the diagnostic value of AMBER and HRCT in the evaluation of bronchiectasis. A series of 50 patients with conventional X-ray findings suggestive of this condition (increased pulmonary markings, loss of pulmonary volume and segmental cysts) were submitted to HRCT. In all the patients with bronchiectasis (25/50), AMBER showed increased pulmonary markings in one or more localizations, while loss of pulmonary volume was observed in 22 cases and segmental cysts in 8. The positive predictive value (PPV) of these findings was 50% for increased markings, 59% for the loss of pulmonary volume, 64% for the association of the former two signs and finally 100% for segmental cysts. The false-positive cases were due to bronchial wall thickening and to areas of peribronchial fibrosis. On the basis of their findings, the authors conclude that, except for the finding of segmental cysts, AMBER does not allow the unquestionable diagnosis of bronchiectasis to be made due to its low PPV (64%) and that further studies with HRCT are therefore required.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Hennegoides Africanus sp. nov. from Kadey River in Cameroon.
Acta Parasitol
January 2025
Laboratory of Parasitology and Ecology, Department of Animal Biology and Physiology, Faculty of Sciences, University of Yaoundé I, P.O. Box 812, Yaoundé, Cameroon.
Purpose: Fish are susceptible to various parasitic infections, with Myxozoa emerging as a major group. A taxonomic study of Myxozoa is essential for the rapid diagnosis of species potentially responsible for epizootic diseases.
Methods: The studied fish was collected from the Kadey River, a tributary of the Sangha River in the Congo Basin in Cameroon, and parasitologically dissected.
Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients.
Front Oncol
January 2025
Department of Radiology, Ordos Central Hospital, Ordos, Inner Mongolia, China.
Background: Improvements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.
Objectives: The purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.
Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review.
medRxiv
January 2025
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Background: Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy.
View Article and Find Full Text PDFPulmonary hydatid cyst misdiagnosed as lung cancer.
IDCases
December 2024
Department of Cardiac and Thoracic Surgery, The Military Hospital of Instruction of Tunis, Tunisia.
Hydatid disease is endemic in Tunisia. Whereas uncomplicated pulmonary hydatid cysts are easily diagnosed on radiological findings, complicated and atypical forms may be misdiagnosed and confused with other pulmonary lesions, mainly lung malignancies. We report a case of a 47-year-old woman, who presented with a 3-month history of hemoptysis.
View Article and Find Full Text PDFDifferentiating Cystic Lesions in the Sellar Region of the Brain Using Artificial Intelligence and Machine Learning for Early Diagnosis: A Prospective Review of the Novel Diagnostic Modalities.
Cureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
View Article and Find Full Text PDFWant 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!