AIMC Topic: Magnetic Resonance Imaging
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Clinician Perspectives of a Magnetic Resonance Imaging-Based 3D Volumetric Analysis Tool for Neurofibromatosis Type 2-Related Schwannomatosis: Qualitative Pilot Study.
JMIR human factors
Jul 30, 2025
BACKGROUND: Accurate monitoring of tumor progression is crucial for optimizing outcomes in neurofibromatosis type 2-related schwannomatosis. Standard 2D linear analysis on magnetic resonance imaging is less accurate than 3D volumetric analysis, but s...
AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons.
Scientific data
Jul 29, 2025
The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by releasing fully reproducible nnU-Net models, along with AI-assisted segmentation for cancer radiology i...
A hybrid filtering and deep learning approach for early Alzheimer's disease identification.
Scientific reports
Jul 29, 2025
Alzheimer's disease is a progressive neurological disorder that profoundly affects cognitive functions and daily activities. Rapid and precise identification is essential for effective intervention and improved patient outcomes. This research introdu...
Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.
Scientific reports
Jul 29, 2025
Assessing MGMT promoter methylation is crucial for determining appropriate glioblastoma therapy. Previous studies have focused on intratumoral regions, overlooking the peritumoral area. This study aimed to develop a radiomic model using MRI-derived f...
Exploring Neural Idiosyncrasies in Response to Autonomous Sensory Meridian Response Videos: Naturalistic Functional Magnetic Resonance Imaging Study of Stress and Sensory Processing.
Journal of medical Internet research
Jul 29, 2025
BACKGROUND: Autonomous sensory meridian response (ASMR) videos have been increasingly popularized as accessible tools for stress relief. Despite widespread media coverage promoting their benefits, empirical research on the neural mechanisms underlyin...
Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.
BMC cancer
Jul 28, 2025
PURPOSE: To predict the 1p/19q molecular status of Lower-grade glioma (LGG) patients nondestructively, this study developed a deep learning (DL) approach using radiomic to provide a potential decision aid for clinical determination of molecular strat...
A new low-rank adaptation method for brain structure and metastasis segmentation via decoupled principal weight direction and magnitude.
Scientific reports
Jul 28, 2025
Deep learning techniques have become pivotal in medical image segmentation, but their success often relies on large, manually annotated datasets, which are expensive and labor-intensive to obtain. Additionally, different segmentation tasks frequently...
Optimization of deep learning models for inference in low resource environments.
Computers in biology and medicine
Jul 26, 2025
Artificial Intelligence (AI), and particularly deep learning (DL), has shown great promise to revolutionize healthcare. However, clinical translation is often hindered by demanding hardware requirements. In this study, we assess the effectiveness of ...
Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.
Annals of medicine
Jul 25, 2025
BACKGROUND: Extrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear.
DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI.
Computers in biology and medicine
Jul 24, 2025
A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience...
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