AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis.

Computational and mathematical methods in medicine Sep 12, 2021
Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with dia...
Computational Biology Algorithms Pneumonia Databases, Factual Radiographic Image Interpretation, Computer-Assisted Humans SARS-CoV-2 Deep Learning Neural Networks, Computer COVID-19
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Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery Sep 10, 2021
OBJECTIVE: The aim of this study was to evaluate an automatic, deep learning based method (Augmented Radiology for Vascular Aneurysm [ARVA]), to detect and assess maximum aortic diameter, providing cross sectional outer to outer aortic wall measureme...
Automation Computed Tomography Angiography Aortography Predictive Value of Tests Aortic Aneurysm, Abdominal Radiographic Image Interpretation, Computer-Assisted Aged, 80 and over Young Adult Adult Male Middle Aged Deep Learning Treatment Outcome Retrospective Studies Female Humans Reproducibility of Results Neural Networks, Computer Aged Databases, Factual
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Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.

Radiology Sep 7, 2021
Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of...
Humans United States Mass Screening Mammography Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Breast Breast Neoplasms Prospective Studies Middle Aged Female Case-Control Studies Reproducibility of Results Deep Learning
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Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Radiology Sep 7, 2021
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and cla...
Deep Learning Bone Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Female Adult Male Bone and Bones Humans Retrospective Studies
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Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.

Diagnostic and interventional imaging Sep 5, 2021
PURPOSE: The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.
Humans Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Phantoms, Imaging Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted
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Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study.

Medical physics Sep 1, 2021
PURPOSE: To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm.
Image Processing, Computer-Assisted Algorithms Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Deep Learning Radiation Dosage Drug Tapering
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Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs.

Radiology Aug 31, 2021
Background A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT. Purpose To evaluate whether a deep learning (DL)-based CAD system can improve di...
Tuberculosis, Pulmonary Radiography, Thoracic Lung Deep Learning Humans Cohort Studies Sensitivity and Specificity Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Male Female Middle Aged
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Exploring the diagnostic effectiveness for myocardial ischaemia based on CCTA myocardial texture features.

BMC cardiovascular disorders Aug 31, 2021
BACKGROUND: To explore the characteristics of myocardial textures on coronary computed tomography angiography (CCTA) images in patients with coronary atherosclerotic heart disease, a classification model was established, and the diagnostic effectiven...
Middle Aged Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Humans Coronary Artery Disease Computed Tomography Angiography Aged Machine Learning Proof of Concept Study Male Retrospective Studies Myocardial Ischemia Female Multidetector Computed Tomography
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A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning.

Computers in biology and medicine Aug 25, 2021
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To...
Humans Computers Tomography, X-Ray Computed Sensitivity and Specificity Lung Radiographic Image Interpretation, Computer-Assisted Deep Learning Lung Neoplasms Solitary Pulmonary Nodule
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A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

Scientific reports Aug 23, 2021
Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of rea...
Female Aged Adult Male Sensitivity and Specificity Humans Middle Aged Machine Learning Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Intracranial Hemorrhages
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