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Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study.
Insights Imaging
January 2025
Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Introduction: A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.
Materials And Methods: Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals.
Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance.
Insights Imaging
January 2025
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Objectives: To investigate the image quality and diagnostic performance with ultra-low dose dual-layer detector spectral CT (DLSCT) by various reconstruction techniques for evaluation of pulmonary nodules.
Materials And Methods: Between April 2023 and December 2023, patients with suspected pulmonary nodules were prospectively enrolled and underwent regular-dose chest CT (RDCT; 120 kVp/automatic tube current) and ultra-low dose CT (ULDCT; 100 kVp/10 mAs) on a DLSCT scanner. ULDCT was reconstructed with hybrid iterative reconstruction (HIR), electron density map (EDM), and virtual monoenergetic images at 40 keV and 70 keV.
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning.
Med Image Comput Comput Assist Interv
October 2024
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.
View Article and Find Full Text PDFIntroduction Chronic obstructive pulmonary disease (COPD) is a significant contributor to global morbidity and mortality. Despite well-established management protocols, treatment remains suboptimal due to high costs and mortality rates. This study aims to compare the impact of initial oxygenation status, Dyspnea, Eosinopenia, Consolidation, Acidemia, and Atrial Fibrillation (DECAF), and National Early Warning Score 2 (NEWS2) scores on management outcomes in COPD patients.
View Article and Find Full Text PDFLemierre's Syndrome in a 16-Year-Old Man Associated with Resolution of Marked Widespread St Elevation and a Normal Troponin Level.
Eur J Case Rep Intern Med
December 2024
Respiratory Department, Barnsley District General Hospital, Barnsley, UK.
Unlabelled: A 16-year-old man presented to the Accident and Emergence services with a 10-day history of shortness of breath, sore throat, vomiting, diarrhoea, poor oral intake, chest pain, jaundice, diplopia and reduced urine output. He was initially treated for sepsis, however, subsequent imaging and blood cultures confirmed the diagnosis of Lemierre's syndrome (LS). LS, also known as necrobacillosis or post-pharyngitis anaerobic septicaemia is comprised of a triad of metastatic septic emboli secondary to pharyngitis, bacteraemia, and internal jugular vein thrombophlebitis.
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