AIMC Journal:
European radiology

Showing 601 to 610 of 646 articles

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

European radiology Apr 1, 2019
OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Hip fracture is a leading worldwide health problem for the elde...
Female Aged Aged, 80 and over Young Adult Adult Humans Neural Networks, Computer Algorithms Prognosis Radiographic Image Interpretation, Computer-Assisted Feasibility Studies Hip Fractures Sensitivity and Specificity Male Middle Aged Deep Learning ROC Curve False Negative Reactions Radiography, Abdominal
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Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.

European radiology Mar 29, 2019
OBJECTIVES: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).
ROC Curve Ultrasonography, Mammary Image Interpretation, Computer-Assisted Observer Variation Female Humans Aged Neural Networks, Computer Adult Algorithms Breast Neoplasms Retrospective Studies Middle Aged Clinical Decision-Making
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Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy.

European radiology Mar 26, 2019
OBJECTIVES: To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions.
Biopsy, Needle Pneumothorax ROC Curve Drainage Area Under Curve Neural Networks, Computer Radiography Retrospective Studies Female Image-Guided Biopsy Middle Aged Sensitivity and Specificity Male Lung Diagnosis, Computer-Assisted Deep Learning Humans
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The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR, or high-risk plaque features?

European radiology Mar 22, 2019
OBJECTIVES: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically s...
Myocardial Ischemia Plaque, Atherosclerotic Coronary Stenosis Fractional Flow Reserve, Myocardial Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Male Middle Aged Coronary Angiography
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Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.

European radiology Mar 15, 2019
PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.
Neural Networks, Computer Algorithms Retrospective Studies Tomography, X-Ray Computed Female Humans Neck Lymphatic Metastasis Area Under Curve Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Male Diagnosis, Computer-Assisted Deep Learning Thyroid Neoplasms
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Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images.

European radiology Mar 11, 2019
OBJECTIVE: To predict the local recurrence of giant cell bone tumors (GCTB) on MR features and the clinical characteristics after curettage using a deep convolutional neural network (CNN).
Humans Curettage Logistic Models Follow-Up Studies Magnetic Resonance Imaging Bone Neoplasms Young Adult Neoplasm Staging Adult Neural Networks, Computer Male Neoplasm Recurrence, Local Algorithms Middle Aged Image Interpretation, Computer-Assisted Prognosis Adolescent Preoperative Period Female Bone and Bones Giant Cell Tumor of Bone
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Potential of a machine-learning model for dose optimization in CT quality assurance.

European radiology Feb 19, 2019
OBJECTIVES: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.
Male Middle Aged Adolescent Quality Assurance, Health Care Radiation Dosage Radiography, Thoracic Multidetector Computed Tomography Radiation Injuries Thoracic Diseases Aged Humans Machine Learning Retrospective Studies Aged, 80 and over Female Young Adult Adult Cross-Sectional Studies
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Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.

European radiology Feb 12, 2019
OBJECTIVE: To determine the possible influence of segmentation margin on each step (feature reproducibility, selection, and classification) of the machine learning (ML)-based high-dimensional quantitative computed tomography (CT) texture analysis (qC...
Female Kidney Neoplasms Machine Learning Reproducibility of Results Humans Algorithms Carcinoma, Renal Cell Male Retrospective Studies Middle Aged Tomography, X-Ray Computed Diagnosis, Differential
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Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning.

European radiology Feb 12, 2019
OBJECTIVES: Distinguishing between kidney stones and phleboliths can constitute a diagnostic challenge in patients undergoing unenhanced low-dose CT (LDCT) for acute flank pain. We sought to investigate the accuracy of radiomics and a machine-learnin...
Kidney Calculi Lithiasis Acute Pain Flank Pain Tomography, X-Ray Computed Adult Female Male Humans Aged Middle Aged Machine Learning Aged, 80 and over Diagnosis, Differential Algorithms Young Adult
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Machine learning identifies "rsfMRI epilepsy networks" in temporal lobe epilepsy.

European radiology Feb 8, 2019
OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning met...
Machine Learning Female Young Adult Adult Male Magnetic Resonance Imaging Electroencephalography Cerebellum Epilepsy, Temporal Lobe Thalamus Humans
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