AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.

European journal of radiology Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Middle Aged Contrast Media Neoplasm Grading Radiographic Image Interpretation, Computer-Assisted Adult Sensitivity and Specificity Radiomics Male Humans Machine Learning Reproducibility of Results Aged Kidney Neoplasms Aged, 80 and over Carcinoma, Renal Cell Tomography, X-Ray Computed Boosting Machine Learning Algorithms Female
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Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data.

BMC medical imaging Apr 29, 2025
BACKGROUND: To assess the effect of the combination of deep learning reconstruction (DLR) and time-resolved maximum intensity projection (tMIP) or time-resolved average (tAve) post-processing method on image quality of CTA derived from low-dose cereb...
Image Processing, Computer-Assisted Adult Radiographic Image Interpretation, Computer-Assisted Male Radiation Dosage Computed Tomography Angiography Humans Middle Aged Signal-To-Noise Ratio Retrospective Studies Deep Learning Female Aged
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Application and optimization of the U-Net++ model for cerebral artery segmentation based on computed tomographic angiography images.

European journal of radiology Apr 27, 2025
Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovascular diseases, including ischemic stroke. This study implemented a deep learning-based U-Net++ mode...
Humans Reproducibility of Results Deep Learning Radiographic Image Interpretation, Computer-Assisted Cerebrovascular Disorders Computed Tomography Angiography Cerebral Angiography Cerebral Arteries
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Reduction of radiation exposure in chest radiography using deep learning-based noise reduction processing: A phantom and retrospective clinical study.

Radiography (London, England : 1995) Apr 24, 2025
INTRODUCTION: Intelligent noise reduction (INR), a deep learning-based noise reduction developed by Canon, is used in planar radiography to improve image quality and reduce patient exposure dose. This study aimed to evaluate the reduction of patient ...
Radiation Exposure Aged Radiographic Image Interpretation, Computer-Assisted Adult Humans Radiation Dosage Male Retrospective Studies Radiography, Thoracic Middle Aged Female Phantoms, Imaging Deep Learning
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Impact of CT reconstruction algorithms on pericoronary and epicardial adipose tissue attenuation.

European journal of radiology Apr 23, 2025
OBJECTIVE: This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adi...
Humans Algorithms Retrospective Studies Tomography, X-Ray Computed Female Pericardium Epicardial Adipose Tissue Adipose Tissue Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Computed Tomography Angiography Aged Male Middle Aged Deep Learning
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Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification.

Artificial intelligence in medicine Apr 23, 2025
U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our s...
Algorithms Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Thoracic Diseases Humans
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Deep learning-based post hoc denoising for 3D volume-rendered cardiac CT in mitral valve prolapse.

The international journal of cardiovascular imaging Apr 23, 2025
We hypothesized that deep learning-based post hoc denoising could improve the quality of cardiac CT for the 3D volume-rendered (VR) imaging of mitral valve (MV) prolapse. We aimed to evaluate the quality of denoised 3D VR images for visualizing MV pr...
Male Middle Aged Deep Learning Imaging, Three-Dimensional Mitral Valve Mitral Valve Prolapse Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Female Reproducibility of Results Humans Aged Retrospective Studies
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Torg-Pavlov ratio qualification to diagnose developmental cervical spinal stenosis based on HRViT neural network.

BMC musculoskeletal disorders Apr 23, 2025
BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, c...
Deep Learning Radiographic Image Interpretation, Computer-Assisted Cervical Vertebrae Spinal Stenosis Humans Young Adult Neural Networks, Computer Adult Spinal Canal Female Male Middle Aged
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Plaque burden improves the detection of ischemic CAD over stenosis from coronary computed tomography angiography.

The international journal of cardiovascular imaging Apr 22, 2025
In symptomatic patients undergoing coronary CTA for suspected coronary artery disease (CAD), we assessed if quantification of plaque burden, in addition to luminal narrowing and clinical risk factors, offers incremental value for the identification o...
Vascular Calcification Computed Tomography Angiography Myocardial Perfusion Imaging Coronary Stenosis Severity of Illness Index Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Coronary Vessels Multidetector Computed Tomography Plaque, Atherosclerotic Risk Factors Aged Male Middle Aged Artificial Intelligence Retrospective Studies Female Humans Reproducibility of Results
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Using deep learning for estimation of time-since-injury in pediatric accidental fractures.

Pediatric radiology Apr 22, 2025
BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skele...
Infant, Newborn Sensitivity and Specificity Humans Male Retrospective Studies Infant Radiographic Image Interpretation, Computer-Assisted Female Deep Learning Accidental Injuries Child, Preschool Child Fractures, Bone
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