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Lung ultrasound for assessing disease progression in UIP and NSIP: a comparative study with HRCT and PFT/DLCO.
BMC Pulm Med
January 2025
Tehran Lung Research and Developmental Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: This study aims to compare Lung Ultrasound (LUS) findings with High-Resolution Computerized Tomography (HRCT) and Pulmonary Function Tests (PFTs) to detect the severity of lung involvement in patients with Usual Interstitial Pneumonia (UIP) and Non-Specific Interstitial Pneumonia (NSIP).
Methods: A cross-sectional study was conducted on 35 UIP and 30 NSIP patients at a referral hospital. All patients underwent LUS, HRCT, and PFT.
Machine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis.
Clin Rheumatol
January 2025
Department of Interventional Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, People's Republic of China.
Objectives: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex interaction of clinical variables, and routine extensive US is impractical. Machine learning (ML) models, particularly those integrating the 18-joint ultrasound scoring system (US18), have shown potential to address these issues but remain underexplored.
View Article and Find Full Text PDFDiagnostic performance of prehospital EFAST in predicting CT scan injuries in severe trauma patients: a multicenter cohort study.
Eur J Trauma Emerg Surg
January 2025
Department of Emergency Medicine, Assistance publique des hôpitaux de Marseille (APHM), Marseille University, Timone University Hospital, Marseille, France.
Background: The early mortality of trauma patients, mainly from hemorrhagic shock, raises interest in detecting the presence of non-exteriorized bleeding. Intra-hospital EFAST (Extended Focused Assessment with Sonography for Trauma) has demonstrated its utility in the assessment and management of severe trauma patients (STP). However, there is a lack of data regarding the diagnostic performance of prehospital EFAST (pEFAST).
View Article and Find Full Text PDFG-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.
Int J Comput Assist Radiol Surg
January 2025
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Purpose: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
Methods: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e.
Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis.
Sci Rep
January 2025
Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Automated tools for quantification of idiopathic pulmonary fibrosis (IPF) can aid in ensuring reproducibility, however their complexity and costs can differ substantially. In this retrospective study, two automated tools were compared in 45 patients with biopsy proven (12/45) and imaging-based (33/45) IPF diagnosis (mean age 74 ± 9 years, 37 male) for quantification of pulmonary fibrosis in CT. First, a tool that identifies multiple characteristic lung texture features was applied to measure multi-texture fibrotic lung (MTFL) by combining the amount of ground glass, reticulation, and honeycombing.
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