AIMC Topic: Magnetic Resonance Imaging
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Deep Learning for Lumbar Spine MRI Reporting: A Welcome Tool for Radiologists.
Radiology
May 11, 2021
Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.
Radiology
May 11, 2021
Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming. Deep learning (DL) could improve -productivity and the consistency of reporting. Purpose To develop a DL model for automated detection and classification of lumb...
Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.
Scientific reports
May 11, 2021
Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning bot...
Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease.
BMC neurology
May 11, 2021
BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, ...
Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.
Human brain mapping
May 10, 2021
The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In th...
Application of artificial intelligence in gynecologic malignancies: A review.
The journal of obstetrics and gynaecology research
May 10, 2021
With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiogra...
Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
May 10, 2021
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. -space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of -space...
Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type.
Scientific reports
May 10, 2021
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic var...
Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.
Scientific reports
May 10, 2021
To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic ...
Muscles
Polymyositis
Urokinase-Type Plasminogen Activator
Antibodies, Antinuclear
Diagnosis, Differential
Biopsy
Dermatomyositis
Image Interpretation, Computer-Assisted
Antigens, Ly
Female
Reproducibility of Results
Aged
Adult
Male
Middle Aged
ROC Curve
Magnetic Resonance Imaging
Humans
Machine Learning
Retrospective Studies
Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.
Cancer medicine
May 8, 2021
BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning m...
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