AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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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...
Mammography Color Radiographic Image Interpretation, Computer-Assisted Breast Neoplasms Middle Aged Diagnosis, Computer-Assisted Deep Learning Humans Retrospective Studies Female
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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...
Aged Contrast Media Humans Aged, 80 and over Middle Aged Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Adult Tomography, X-Ray Computed Deep Learning Artifacts Male Female Iodine Radiography, Dual-Energy Scanned Projection Head and Neck Neoplasms
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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 Risk Assessment Deep Learning Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Female Aged Male Middle Aged Radiation Dosage
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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...
Image Processing, Computer-Assisted Lung Neoplasms Logistic Models Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Algorithms Tomography, X-Ray Computed Humans Reproducibility of Results Machine Learning Radiomics
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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...
Humans Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Spinal Cord Signal-To-Noise Ratio Computed Tomography Angiography Radiography, Dual-Energy Scanned Projection Male Female Middle Aged Aged Deep Learning Aged, 80 and over Contrast Media Adult
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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).
Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Coronary Artery Disease Humans Female Vascular Calcification Artificial Intelligence Reproducibility of Results Retrospective Studies Sweden
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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 Radiomics Clinical Competence Mammography Machine Learning Radiology Female Radiographic Image Interpretation, Computer-Assisted Adult Breast Neoplasms
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Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

Journal of computer assisted tomography Oct 10, 2024
OBJECTIVE: The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image quality of low-dose computed tomography pulmonary angiography at reduced iodine load.
Pulmonary Artery Pulmonary Embolism Radiation Dosage Computed Tomography Angiography Radiographic Image Enhancement Aged, 80 and over Adult Male Algorithms Middle Aged Retrospective Studies Deep Learning Female Iodine Aged Contrast Media Humans Radiographic Image Interpretation, Computer-Assisted
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An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Oct 10, 2024
The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and cli...
Solitary Pulmonary Nodule Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Deep Learning Lung Neoplasms Humans
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Energy-integrating detector based ultra-high-resolution CT with deep learning reconstruction for the assessment of calcified lesions in coronary artery disease.

Journal of cardiovascular computed tomography Oct 8, 2024
BACKGROUND: The aim of this study to compare of the image quality of calcified lesions in coronary artery disease between deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR) on energy-integrating detector (EID) based ul...
Phantoms, Imaging Computed Tomography Angiography Aged Male Middle Aged Humans Severity of Illness Index Coronary Artery Disease Deep Learning Retrospective Studies Coronary Vessels Predictive Value of Tests Female Multidetector Computed Tomography Coronary Angiography Reproducibility of Results Vascular Calcification Radiographic Image Interpretation, Computer-Assisted
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