AIMC Topic: Magnetic Resonance Imaging

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Fractal analysis for cognitive impairment classification in DAVF using machine learning.

Biomedical physics & engineering express
. Intracranial dural arteriovenous fistula (DAVF) is an acquired vascular condition involving abnormal connections between dural arteries and veins without intervening capillary beds. Cognitive impairment is a common symptom in DAVFs, often linked to...

Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.

Cancer medicine
OBJECTIVES: Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic effica...

Updates and advances for gynecologic imaging.

The journal of obstetrics and gynaecology research
A gynecologic malignancy is one of the most common cancers affecting females and is responsible for significant rates of morbidity and mortality throughout the world. Early discovery and accurate staging, as well as early recurrence detection and cor...

Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.

Korean journal of radiology
OBJECTIVE: To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weigh...

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Yonsei medical journal
PURPOSE: Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-proce...

Brain Age Prediction: Deep Models Need a Hand to Generalize.

Human brain mapping
Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns abou...

An Explainable Connectome Convolutional Transformer for Multimodal Autism Spectrum Disorder Classification.

International journal of neural systems
The diagnosis of autism spectrum disorder (ASD) is often hampered by its heterogeneity and reliance on time-consuming behavioral assessments. Automated neuroimaging-based diagnostic tools offer a promising alternative, but multi-site data integration...

Insights on Scan-Specific Deep-Learning Strategies for Brain MRI Parallel Imaging Reconstruction.

NMR in biomedicine
Scan-specific deep learning strategies have been proposed for parallel imaging reconstruction in which auto-calibrated signals (ACS) are used for training. Here, we introduce methods to objectively optimize architecture and training details. In addit...

Does restrictive anorexia nervosa impact brain aging? A machine learning approach to estimate age based on brain structure.

Computers in biology and medicine
Anorexia nervosa (AN), a severe eating disorder marked by extreme weight loss and malnutrition, leads to significant alterations in brain structure. This study used machine learning (ML) to estimate brain age from structural MRI scans and investigate...

Selection, visualization, and explanation of deep features from resting-state fMRI for Alzheimer's disease diagnosis.

Psychiatry research. Neuroimaging
Despite the remarkable achievements of deep learning networks in analyzing neuroimaging data for various tasks linked to brain functions and disorders, the opaque nature of these models and their interpretability challenges pose significant barriers ...