AIMC Journal:
European radiology
Showing 521 to 530 of 639 articles
Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?
European radiology
May 6, 2020
Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and c...
Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
European radiology
Apr 29, 2020
OBJECTIVES: To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.
Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study.
European radiology
Apr 29, 2020
OBJECTIVES: To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (...
Intracranial Aneurysm
Clinical Decision Rules
Area Under Curve
Young Adult
Computed Tomography Angiography
Adult
Cerebral Angiography
Male
Aneurysm, Ruptured
Middle Aged
Retrospective Studies
Adolescent
Humans
Tomography, X-Ray Computed
Hemodynamics
Logistic Models
Neural Networks, Computer
China
Support Vector Machine
Machine Learning
Female
Computer Simulation
Aged
Aged, 80 and over
Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
European radiology
Apr 29, 2020
OBJECTIVES: To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs.
Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.
European radiology
Apr 26, 2020
OBJECTIVE: To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM).
High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.
European radiology
Apr 23, 2020
OBJECTIVE: To assess if adding perfusion information from dynamic contrast-enhanced (DCE MRI) acquisition schemes with high spatiotemporal resolution to T2w/DWI sequences as input features for a gradient boosting machine (GBM) machine learning (ML) c...
Development of automatic measurement for patellar height based on deep learning and knee radiographs.
European radiology
Apr 23, 2020
OBJECTIVES: To develop and evaluate the performance of a deep learning-based system for automatic patellar height measurements using knee radiographs.
AI for reading screening mammograms: the need for circumspection.
European radiology
Apr 21, 2020
• The studies on AI reading of screening mammograms have methodological limitations that undermine the conclusion that AI could do better than radiologists. • These studies do not informon numbers of extra breast cancers found by AI that could repres...
Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.
European radiology
Apr 8, 2020
OBJECTIVES: To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification...
Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
European radiology
Mar 28, 2020
OBJECTIVES: We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods.
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