AIMC Topic: Brain
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Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy.
Magnetic resonance imaging
Oct 18, 2024
BACKGROUND: Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults. Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however...
Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.
Computers in biology and medicine
Oct 18, 2024
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...
Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.
Computers in biology and medicine
Oct 18, 2024
PURPOSE: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the disc...
Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.
Omics : a journal of integrative biology
Oct 17, 2024
It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are no curative pharmacological treatments for ASD. Discoveries on key molecular mechanisms of ASD are es...
Deep plug-and-play MRI reconstruction based on multiple complementary priors.
Magnetic resonance imaging
Oct 16, 2024
Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method ...
Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.
Neural networks : the official journal of the International Neural Network Society
Oct 15, 2024
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which ...
A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.
Journal of Alzheimer's disease : JAD
Oct 14, 2024
BACKGROUND: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors.
A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Oct 13, 2024
A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing interscan and intersubject variability. By training a binary linear SVM classifier with PET datasets from t...
A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.
Talanta
Oct 11, 2024
Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vul...
Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.
IEEE transactions on visualization and computer graphics
Oct 11, 2024
Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise...
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