AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Histological proven AI performance in the UKLS CT lung cancer screening study: Potential for workload reduction.
European journal of cancer (Oxford, England : 1990)
Feb 25, 2025
PURPOSE: Artificial intelligence (AI) could reduce lung cancer screening computer tomography (CT)-reading workload if used as a first-reader, ruling-out negative CT-scans at baseline. Evidence is lacking to support AI performance when compared to gol...
Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.
Academic radiology
Feb 24, 2025
RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-...
Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 23, 2025
BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD), the leading cause of mortality, is caused by atherosclerotic plaque buildup in the arteries. The gold standard for the diagnosis of CAD is via X-ray coronary angiography (XCA) during percutaneo...
Multi-dimensional consistency learning between 2D Swin U-Net and 3D U-Net for intestine segmentation from CT volume.
International journal of computer assisted radiology and surgery
Feb 22, 2025
PURPOSE: The paper introduces a novel two-step network based on semi-supervised learning for intestine segmentation from CT volumes. The intestine folds in the abdomen with complex spatial structures and contact with neighboring organs that bring dif...
Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis.
Journal of cardiovascular computed tomography
Feb 22, 2025
BACKGROUND: As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamic...
Humans
Machine Learning
Hydrodynamics
Computed Tomography Angiography
Models, Cardiovascular
Coronary Stenosis
Coronary Angiography
Reproducibility of Results
Fractional Flow Reserve, Myocardial
Radiographic Image Interpretation, Computer-Assisted
Coronary Artery Disease
Severity of Illness Index
Coronary Vessels
Predictive Value of Tests
Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.
Cardiovascular engineering and technology
Feb 20, 2025
The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such ...
Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.
European journal of radiology
Feb 20, 2025
BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but remains time-consuming. Although UNet is widely used in medical imaging, its performance is limited by the inhe...
Deep learning based coronary vessels segmentation in X-ray angiography using temporal information.
Medical image analysis
Feb 18, 2025
Invasive coronary angiography (ICA) is the gold standard imaging modality during cardiac interventions. Accurate segmentation of coronary vessels in ICA is required for aiding diagnosis and creating treatment plans. Current automated algorithms for v...
Prior knowledge-based multi-task learning network for pulmonary nodule classification.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 16, 2025
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...
Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.
European journal of radiology
Feb 15, 2025
PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the ...
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