AIMC Topic:
Magnetic Resonance Imaging
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Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.
Medical physics
Jun 24, 2024
BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of ...
[Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization].
Radiologie (Heidelberg, Germany)
Jun 24, 2024
BACKGROUND: Artificial intelligence (AI) has the potential to fundamentally change radiology workflow.
An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.
BMC medical imaging
Jun 24, 2024
Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) ...
The role of artificial intelligence in cardiovascular magnetic resonance imaging.
Progress in cardiovascular diseases
Jun 24, 2024
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-o...
Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.
Journal of gastroenterology and hepatology
Jun 23, 2024
BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the perfo...
Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.
Journal of neurology
Jun 23, 2024
BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.
Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Jun 22, 2024
BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently a...
Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results.
Journal of magnetic resonance imaging : JMRI
Jun 22, 2024
BACKGROUND: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors.
Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks.
The spine journal : official journal of the North American Spine Society
Jun 22, 2024
BACKGROUND CONTEXT: Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonan...
Siamese Graph Convolutional Network quantifies increasing structure-function discrepancy over the cognitive decline continuum.
Computer methods and programs in biomedicine
Jun 22, 2024
BACKGROUND AND OBJECTIVE: Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, whic...
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