AIMC Journal:
European radiology

Showing 451 to 460 of 621 articles

Artificial Intelligence-assisted chest X-ray assessment scheme for COVID-19.

European radiology Jan 20, 2021
OBJECTIVES: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)-positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinic...
X-Rays SARS-CoV-2 Radiography, Thoracic Humans COVID-19 Artificial Intelligence Tomography, X-Ray Computed
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Deep learning in breast radiology: current progress and future directions.

European radiology Jan 15, 2021
This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be int...
Humans Breast Artificial Intelligence Prospective Studies Deep Learning Radiology
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Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.

European radiology Jan 14, 2021
OBJECTIVE: To investigate the application of machine learning-based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer.
Machine Learning Retrospective Studies ROC Curve Liver Neoplasms Ultrasonography Humans
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Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

European radiology Jan 7, 2021
OBJECTIVE: To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT.
Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Thorax Tomography, X-Ray Computed Deep Learning Humans Algorithms
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Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

European radiology Jan 6, 2021
OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemor...
Cerebral Hemorrhage Brain Edema Reproducibility of Results Deep Learning Intracranial Hemorrhages Edema Humans
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Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver.

European radiology Jan 6, 2021
OBJECTIVES: To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging features on MRI.
Humans Retrospective Studies Deep Learning Contrast Media Magnetic Resonance Imaging Liver Neoplasms Carcinoma, Hepatocellular
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Utility of deep learning for the diagnosis of otosclerosis on temporal bone CT.

European radiology Jan 6, 2021
OBJECTIVE: Diagnosis of otosclerosis on temporal bone CT images is often difficult because the imaging findings are frequently subtle. Our aim was to assess the utility of deep learning analysis in diagnosing otosclerosis on temporal bone CT images.
Humans Temporal Bone Tomography, X-Ray Computed Otosclerosis Deep Learning Radiologists
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Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.

European radiology Jan 3, 2021
OBJECTIVES: We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in compari...
Humans Algorithms Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Drug Tapering Tomography, X-Ray Computed Deep Learning
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The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.

European radiology Dec 28, 2020
OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD).
Tomography, X-Ray Computed Deep Learning Humans COVID-19 Pneumonia, Viral SARS-CoV-2 Neural Networks, Computer Prospective Studies
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Evaluation of a novel deep learning-based classifier for perifissural nodules.

European radiology Dec 2, 2020
OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN).
Lung Neoplasms Multiple Pulmonary Nodules Humans Deep Learning Solitary Pulmonary Nodule Netherlands
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