AIMC Topic: Edema
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Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.
European radiology experimental
Jun 19, 2025
BACKGROUND: In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the auto...
Deep Learning-Driven Abbreviated Shoulder MRI Protocols: Diagnostic Accuracy in Clinical Practice.
Tomography (Ann Arbor, Mich.)
Apr 17, 2025
BACKGROUND: Deep learning (DL) reconstruction techniques have shown promise in reducing MRI acquisition times while maintaining image quality. However, the impact of different acceleration factors on diagnostic accuracy in shoulder MRI remains unexpl...
Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.
Scientific reports
Apr 11, 2025
Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to symptom overlap with conditions like chronic venous insufficiency (CVI), deep vein thrombosis (DVT), a...
Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images.
Journal of magnetic resonance imaging : JMRI
Feb 6, 2025
BACKGROUND: Sacroiliitis is a hallmark of ankylosing spondylitis (AS), and early detection plays an important role in managing the condition effectively. MRI is commonly used for diagnosing sacroiliitis, traditional methods often depend on subjective...
Radiomics
Male
Middle Aged
Adolescent
Young Adult
Adult
Sensitivity and Specificity
Image Interpretation, Computer-Assisted
Edema
Sacroiliac Joint
Sacroiliitis
Child
ROC Curve
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Humans
Retrospective Studies
Algorithms
Female
Reproducibility of Results
Aged
Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.
Scientific reports
Aug 14, 2024
To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enr...
Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.
Physics in medicine and biology
Apr 26, 2024
To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.The primary focus is on develo...
W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 24, 2024
Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework ba...
Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network.
Journal of neuro-oncology
Dec 22, 2023
PURPOSE: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema.
Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages.
Magnetic resonance imaging
Aug 2, 2023
INTRODUCTION: Minimally-invasive surgical techniques for intracerebral hemorrhage (ICH) evacuation use imaging to guide the suction, lysing and/or drainage from the hemorrhage site via various designs. A previous international surgical study has show...
Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.
PloS one
May 8, 2023
PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved m...
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