AIMC Topic: Magnetic Resonance Imaging

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Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization-Based Shepard Convolutional Neural Network.

NMR in biomedicine
The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve problems, like strokes, brain tumors, multiple sclerosis, and dementia, brain magnetic resonance imaging (MRI) is normally utilized. Identifying the tumor o...

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Radiology. Artificial intelligence
Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This mult...

Validation of a Deep Learning-Based Method for Accelerating Susceptibility-Weighted Imaging in Clinical Settings.

NMR in biomedicine
Susceptibility-weighted imaging (SWI) has been widely used in clinical contexts, in which the speed of acquisition is frequently a critical issue. In this study, we aim to test the feasibility of a deep learning (DL)-based reconstruction method for a...

Ovarian-adnexal reporting and data system MRI scoring: diagnostic accuracy, interobserver agreement, and applicability to machine learning.

The British journal of radiology
OBJECTIVES: To evaluate the interobserver agreement and diagnostic accuracy of ovarian-adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning.

Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.

Neuro-oncology
BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning (DL) of magnetic resonance imaging (MRI) tumor f...

MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.

Journal of experimental botany
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...

RRFNet: A free-anchor brain tumor detection and classification network based on reparameterization technology.

PloS one
Advancements in medical imaging technology have facilitated the acquisition of high-quality brain images through computed tomography (CT) or magnetic resonance imaging (MRI), enabling professional brain specialists to diagnose brain tumors more effec...

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

PloS one
INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MR...

Leveraging transformers and explainable AI for Alzheimer's disease interpretability.

PloS one
Alzheimer's disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except ver...

Verity plots: A novel method of visualizing reliability assessments of artificial intelligence methods in quantitative cardiovascular magnetic resonance.

PloS one
BACKGROUND: Artificial intelligence (AI) methods have established themselves in cardiovascular magnetic resonance (CMR) as automated quantification tools for ventricular volumes, function, and myocardial tissue characterization. Quality assurance app...