AIMC Topic: Magnetic Resonance Imaging

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Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
PURPOSE: Tumors are heterogeneous and consist of subregions, also known as tumor habitats, each of which corresponds to a group of tissues with similar structural, metabolic or functional characteristics. This study aims to visualize and quantify int...

MRI grading of lumbar disc herniation based on AFFM-YOLOv8 system.

Scientific reports
Magnetic resonance imaging (MRI) serves as the clinical gold standard for diagnosing lumbar disc herniation (LDH). This multicenter study was to develop and clinically validate a deep learning (DL) model utilizing axial T2-weighted lumbar MRI sequenc...

A multinational study of deep learning-based image enhancement for multiparametric glioma MRI.

Scientific reports
This study aimed to validate the utility of commercially available vendor-neutral deep learning (DL) image enhancement software for improving the image quality of multiparametric MRI for gliomas in a multinational setting. A total of 294 patients fro...

Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy.

Scientific reports
Brain metastases (BMs) are the most common intracranial tumors and stereotactic radiotherapy improved the life quality of patient with BMs, while it requires more time and experience to delineate BMs precisely by oncologists. Deep Learning techniques...

Single-centre, prospective cohort to predict optimal individualised treatment response in multiple sclerosis (POINT-MS): a cohort profile.

BMJ open
PURPOSE: Multiple sclerosis (MS) is a chronic neurological condition that affects approximately 150 000 people in the UK and presents a significant healthcare burden, including the high costs of disease-modifying treatments (DMTs). DMTs have substant...

Machine Learning-Based Classification of White Matter Functional Changes in Stroke Patients Using Resting-State fMRI.

Brain topography
Neuroimaging studies of brain function are important research methods widely applied to stroke patients. Currently, a large number of studies have focused on functional imaging of the gray matter cortex. Relevant research indicates that certain areas...

CNN-based prediction using early post-radiotherapy MRI as a proxy for toxicity in the murine head and neck.

Acta oncologica (Stockholm, Sweden)
BACKGROUND AND PURPOSE: Radiotherapy (RT) of head and neck cancer can cause severe toxicities. Early identification of individuals at risk could enable personalized treatment. This study evaluated whether convolutional neural networks (CNNs) applied ...

A novel hybrid deep learning model for segmentation and uzzy Res-LeNet based classification for Alzheimer's disease.

Neurogenetics
Alzheimer's disease (AD) is a progressive illness that can cause behavioural abnormalities, personality changes, and memory loss. Early detection helps with future planning for both the affected person and caregivers. Thus, an innovative hybrid Deep ...

MRI detection and grading of knee osteoarthritis - a pilot study using an AI technique with a novel imaging-based scoring system.

Biomaterials science
Precise and rapid identification of knee osteoarthritis (OA) is essential for efficient management and therapy planning. Conventional diagnostic techniques frequently depend on subjective interpretation, which have shortcomings, particularly during t...

Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer's disease.

NeuroImage
Alterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer's disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural ...