AIMC Topic: Brain

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brain2print AI powered web tool for creating 3D printable brain models.

Scientific reports
We introduce brain2print, a web-based application that converts T1-weighted MRI scans into 3D printable models. By leveraging artificial intelligence (AI), brain2print performs all computations directly on the user's device, preserving privacy while ...

Machine Learning-Based Identification of Children With Intermittent Exotropia Using Multiple Resting-State Functional Magnetic Resonance Imaging Features.

Brain and behavior
OBJECTIVE: To investigate the performance of machine learning (ML) methods based on resting-state functional magnetic resonance imaging (rs-fMRI) parameters in distinguishing children with intermittent exotropia (IXT) from healthy controls (HCs).

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...

Juvenile Myoclonic Epilepsy Imaging Endophenotypes and Relationship With Cognition and Resting-State EEG.

Human brain mapping
Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: 1-volume reduction of subcortical gray matter structures, and 2-abnormalities of cortical thickness. The general trend has been to obse...

Adaptive Dynamic Surface Control of Epileptor Model Based on Nonlinear Luenberger State Observer.

International journal of neural systems
Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, which are sudden bursts of electrical activity in the brain. The Epileptor model is a computational model specifically created to replicate the complex dynamics of epi...

Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.

Radiology. Artificial intelligence
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...

Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical Referrals.

Radiology. Artificial intelligence
Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emer...

Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health.

Brain : a journal of neurology
Previous research has established type 2 diabetes mellitus as a significant risk factor for various disorders, adversely impacting human health. While evidence increasingly links type 2 diabetes to cognitive impairment and brain disorders, understand...

Neural dynamics of mental state attribution to social robot faces.

Social cognitive and affective neuroscience
The interplay of mind attribution and emotional responses is considered crucial in shaping human trust and acceptance of social robots. Understanding this interplay can help us create the right conditions for successful human-robot social interaction...

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...