AI Medical Compendium Journal:
NeuroImage. Clinical

Showing 81 to 90 of 104 articles

A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning.

NeuroImage. Clinical
BACKGROUND AND PURPOSE: With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided dete...

Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.

NeuroImage. Clinical
Mild traumatic brain injury (mTBI) can result in symptoms that affect a person's cognitive and social abilities. Improvements in diagnostic methodologies are necessary given that current clinical techniques have limited accuracy and are solely based ...

Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.

NeuroImage. Clinical
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in...

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

NeuroImage. Clinical
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiolog...

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.

NeuroImage. Clinical
BACKGROUND: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

NeuroImage. Clinical
Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group...

Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.

NeuroImage. Clinical
OBJECTIVE: To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity.

Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging.

NeuroImage. Clinical
Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver va...

Identification of autism spectrum disorder using deep learning and the ABIDE dataset.

NeuroImage. Clinical
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imagi...

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

NeuroImage. Clinical
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-wei...