AI Medical Compendium Topic:
Neuroimaging

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Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.

Human brain mapping
Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer-aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This stu...

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Brain : a journal of neurology
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's ...

Separable amygdala activation patterns in the evaluations of robots.

Cerebral cortex (New York, N.Y. : 1991)
Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective app...

Deep Learning: A Primer for Neurosurgeons.

Advances in experimental medicine and biology
This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performance, surgical planning, execution, and postoperative assessment. It delves into various deep learning ...

Association between Resting Heart Rate and Machine Learning-Based Brain Age in Middle- and Older-Age.

The journal of prevention of Alzheimer's disease
BACKGROUND: Resting heart rate (RHR), has been related to increased risk of dementia, but the relationship between RHR and brain age is unclear.

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for A...

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.

Journal of Alzheimer's disease : JAD
BACKGROUND: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming...

Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.

Radiology. Artificial intelligence
Purpose To develop a fully automated device- and sequence-independent convolutional neural network (CNN) for reliable and high-throughput labeling of heterogeneous, unstructured MRI data. Materials and Methods Retrospective, multicentric brain MRI da...