AIMC Topic: Corpus Callosum
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Deciphering seizure semiology in corpus callosum injuries: A comprehensive systematic review with machine learning insights.
Clinical neurology and neurosurgery
May 7, 2024
INTRODUCTION: Seizure disorders have often been found to be associated with corpus callosum injuries, but in most cases, they remain undiagnosed. Understanding the clinical, electrographic, and neuroradiological alternations can be crucial in delinea...
Deep learning-based diffusion tensor image generation model: a proof-of-concept study.
Scientific reports
Feb 5, 2024
This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers we...
A simulation study to investigate the use of concentric tube robots for epilepsy surgery.
Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Jan 27, 2022
PURPOSE: Patients with pharmacoresistant refractory epilepsy may require epilepsy surgery to prevent future seizure occurrences. Conventional surgery consists of a large craniotomy with straight rigid tools with associated outcomes of morbidity, larg...
Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
Jan 26, 2022
BACKGROUND AND PURPOSE: Corpus callosum (CC) atrophy is predictive of future disability in multiple sclerosis (MS). However, current segmentation methods are either labor- or computationally intensive. We therefore developed an automated deep learnin...
Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment.
PloS one
Dec 23, 2021
BACKGROUND: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysi...
Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
Feb 15, 2021
BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCC...
Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.
NeuroImage
Oct 6, 2020
The intra-axonal water exchange time (τ), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensiti...
Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.
NeuroImage. Clinical
Apr 24, 2019
The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine l...
A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum.
Medical image analysis
May 3, 2016
In this paper, we extend the one-class Support Vector Machine (SVM) and the regularized discriminative direction analysis to the Multiple Kernel (MK) framework, providing an effective analysis pipeline for the detection and characterization of brain ...
Segmentation with artificial intelligence and automated calculation of the corpus callosum index in multiple sclerosis.
Saudi medical journal
Jun 1, 2025
OBJECTIVES: To determine the corpus callosum index (CCI) differences between chronic phase multiple sclerosis (MS) patients and healthy individuals and to evaluate the corpus callosum segmentation in MS patients using artificial intelligence technolo...
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