AI Medical Compendium Topic:
Neuroimaging

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Clinical Artificial Intelligence Applications in Radiology: Neuro.

Radiologic clinics of North America
Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This ...

Population modeling with machine learning can enhance measures of mental health.

GigaScience
BACKGROUND: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are ...

Brief Report: Neuroimaging Endophenotypes of Social Robotic Applications in Autism Spectrum Disorder.

Journal of autism and developmental disorders
A plethora of neuroimaging studies have focused on the discovery of potential neuroendophenotypes useful to understand the etiopathogenesis of autism and predict treatment response. Social robotics has recently been proposed as an effective tool to s...

Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study.

Brain imaging and behavior
High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular ...

Robot-induced hallucinations in Parkinson's disease depend on altered sensorimotor processing in fronto-temporal network.

Science translational medicine
Hallucinations in Parkinson's disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the i...

Better-than-chance classification for signal detection.

Biostatistics (Oxford, England)
The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is pa...

DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks.

Neuroinformatics
The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain...

Clinical usefulness of deep learning-based automated segmentation in intracranial hemorrhage.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Doctors with various specializations and experience order brain computed tomography (CT) to rule out intracranial hemorrhage (ICH). Advanced artificial intelligence (AI) can discriminate subtypes of ICH with high accuracy.

Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.

Journal of Alzheimer's disease : JAD
BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it...