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Neuroimaging

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Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome...

New developments in imaging in ALS.

Journal of neurology
Neuroimaging in ALS has contributed considerable academic insights in recent years demonstrating genotype-specific topological changes decades before phenoconversion and characterising longitudinal propagation patterns in specific phenotypes. It has ...

[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].

Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain...

Predicting the onset of mental health problems in adolescents.

Psychological medicine
OBJECTIVE: Mental health problems are the major cause of disability among adolescents. Personalized prevention may help to mitigate the development of mental health problems, but no tools are available to identify individuals at risk before they requ...

Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.

Radiology
Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; however, these devices produce lower-quality images than conventional high-field-strength scanners. Pur...

WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.

Human brain mapping
White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly use...

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

NeuroImage
Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its ear...

Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.

Radiology
Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift and precise clinical decisions based on neuroimaging. Recent advances in deep learning-based computer vision and language artificial intelligence (AI)...

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...

Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.

Alzheimer's research & therapy
BACKGROUND: Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves anal...