AI Medical Compendium Topic

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Neuroimaging

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Detecting schizophrenia with 3D structural brain MRI using deep learning.

Scientific reports
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve clas...

I know what you're thinking; can neuroimaging truly reveal our innermost thoughts?

BioTechniques
[Formula: see text] Advances in neuroimaging, combined with developments in artificial intelligence software, have allowed researchers to noninvasively decode the brain and 'read the mind'.

Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on a Norwegian imaging database.

PloS one
An important step in the analysis of magnetic resonance imaging (MRI) data for neuroimaging is the automated segmentation of white matter hyperintensities (WMHs). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particu...

Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging.

Journal of the American College of Radiology : JACR
PURPOSE: The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grown in the past decade. The adoption of these algorithms into clinical practice depends largely on whether this technology provides value in the clinica...

MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Medical image analysis
Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, sub...

Developing and deploying deep learning models in brain magnetic resonance imaging: A review.

NMR in biomedicine
Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models...

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.

Alzheimer's & dementia : the journal of the Alzheimer's Association
BACKGROUND: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies.

Deep learning in neuroimaging of epilepsy.

Clinical neurology and neurosurgery
In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional m...

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence.

Scientific reports
Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descr...