AI Medical Compendium Topic:
Neuroimaging

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Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

Scientific reports
In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine l...

Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.

NeuroImage
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brai...

VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography.

Medical image analysis
Three-dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perfor...

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

Biological psychiatry. Cognitive neuroscience and neuroimaging
Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of...

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.

Epilepsia
OBJECTIVE: Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In ...

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates.

NeuroImage
Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstr...

Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease.

PloS one
Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and health...

Automatically measuring brain ventricular volume within PACS using artificial intelligence.

PloS one
The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and...

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.

Psychiatry research. Neuroimaging
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) o...