AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.
The international journal of cardiovascular imaging
Oct 7, 2024
PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic mal...
Coronary Angiography
Radiographic Image Interpretation, Computer-Assisted
Coronary Artery Disease
Running
Athletes
Male
Middle Aged
Severity of Illness Index
Predictive Value of Tests
Fractional Flow Reserve, Myocardial
Coronary Vessels
Asymptomatic Diseases
Computed Tomography Angiography
Coronary Stenosis
Humans
Artificial Intelligence
Prospective Studies
Reproducibility of Results
Aged
Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.
Neuroradiology
Oct 5, 2024
PURPOSE: To assess the efficacy of radiomics features extracted from non-contrast computed tomography (NCCT) scans in differentiating multiple etiologies of spontaneous intracerebral hemorrhage (ICH).
Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.
Scientific reports
Sep 28, 2024
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improv...
Detection of breast cancer in digital breast tomosynthesis with vision transformers.
Scientific reports
Sep 27, 2024
Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic preci...
Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.
The international journal of cardiovascular imaging
Sep 25, 2024
Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood...
Aged
Radiomics
Male
Middle Aged
Risk Assessment
Nomograms
Predictive Value of Tests
Anticoagulants
Blood Coagulation
Multidetector Computed Tomography
Decision Support Techniques
Computed Tomography Angiography
Humans
Retrospective Studies
Atrial Appendage
Coronary Angiography
Machine Learning
Female
Reproducibility of Results
Radiographic Image Interpretation, Computer-Assisted
Risk Factors
Atrial Fibrillation
Thrombophilia
Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.
Journal of imaging informatics in medicine
Sep 23, 2024
Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. M...
Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising.
Clinical radiology
Sep 21, 2024
AIM: We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using a deep convolutional neural network (CNN).
Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study.
Academic radiology
Sep 20, 2024
RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image q...
Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index.
Journal of imaging informatics in medicine
Sep 19, 2024
To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V). Three ...
Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.
European radiology
Sep 19, 2024
OBJECTIVE: To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) proto...
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