AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Beyond strong labels: Weakly-supervised learning based on Gaussian pseudo labels for the segmentation of ellipse-like vascular structures in non-contrast CTs.

Medical image analysis Oct 30, 2024
Deep learning-based automated segmentation of vascular structures in preoperative CT angiography (CTA) images contributes to computer-assisted diagnosis and interventions. While CTA is the common standard, non-contrast CT imaging has the advantage of...
Normal Distribution Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning Aorta, Abdominal Computed Tomography Angiography Humans Algorithms Deep Learning
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Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography.

European journal of radiology Oct 28, 2024
PURPOSE: To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.
Radiographic Image Enhancement Middle Aged Deep Learning Contrast Media Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Stroke Humans Aged Retrospective Studies Signal-To-Noise Ratio Sensitivity and Specificity Female Computed Tomography Angiography Cerebral Angiography Male
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Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram.

Diagnostic and interventional imaging Oct 26, 2024
PURPOSE: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).
Mammography Humans Radiographic Image Interpretation, Computer-Assisted Software Breast Diseases Retrospective Studies Breast Tomography, X-Ray Computed Vascular Calcification Female Adult Aged Aged, 80 and over Sensitivity and Specificity Middle Aged Deep Learning
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Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

Journal of imaging informatics in medicine Oct 25, 2024
The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of the mammogram based CADx sch...
Color Radiographic Image Interpretation, Computer-Assisted Middle Aged Diagnosis, Computer-Assisted Deep Learning Mammography Humans Retrospective Studies Female Breast Neoplasms
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Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study.

European journal of radiology Oct 25, 2024
PURPOSE: To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT s...
Radiography, Dual-Energy Scanned Projection Female Aged Aged, 80 and over Head and Neck Neoplasms Adult Male Humans Contrast Media Middle Aged Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Deep Learning Artifacts Tomography, X-Ray Computed Iodine
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Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.

Investigative radiology Oct 23, 2024
PURPOSE: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT...
Humans Algorithms Tomography, X-Ray Computed Female Deep Learning Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Aged Male Middle Aged Risk Assessment
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Lung nodule classification using radiomics model trained on degraded SDCT images.

Computer methods and programs in biomedicine Oct 23, 2024
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challeng...
Radiation Dosage Humans Lung Neoplasms Machine Learning Algorithms Logistic Models Tomography, X-Ray Computed Solitary Pulmonary Nodule Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Radiomics Image Processing, Computer-Assisted
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Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Acta radiologica (Stockholm, Sweden : 1987) Oct 22, 2024
BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstru...
Spinal Cord Signal-To-Noise Ratio Computed Tomography Angiography Radiography, Dual-Energy Scanned Projection Aged Aged, 80 and over Adult Male Middle Aged Deep Learning Contrast Media Radiographic Image Interpretation, Computer-Assisted Humans Retrospective Studies Female
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Automated AI-based coronary calcium scoring using retrospective CT data from SCAPIS is accurate and correlates with expert scoring.

European radiology Oct 18, 2024
OBJECTIVES: Evaluation of the correlation and agreement between AI and semi-automatic evaluations of calcium scoring CT (CSCT) examinations using extensive data from the Swedish CardioPulmonary bio-Image study (SCAPIS).
Middle Aged Radiographic Image Interpretation, Computer-Assisted Female Coronary Artery Disease Reproducibility of Results Humans Vascular Calcification Sweden Male Artificial Intelligence Retrospective Studies Tomography, X-Ray Computed
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A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult.

Journal of imaging informatics in medicine Oct 15, 2024
This study aims to investigate whether global mammographic radiomic features (GMRFs) can distinguish hardest- from easiest-to-interpret normal cases for radiology trainees (RTs). Data from 137 RTs were analysed, with each interpreting seven education...
Humans Machine Learning Clinical Competence Mammography Radiology Radiographic Image Interpretation, Computer-Assisted Female Adult Breast Neoplasms Radiomics
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