AI Medical Compendium Topic:
Neuroimaging

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Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors.

Applications of artificial intelligence in neuro-oncology.

Current opinion in neurology
PURPOSE OF REVIEW: To discuss recent applications of artificial intelligence within the field of neuro-oncology and highlight emerging challenges in integrating artificial intelligence within clinical practice.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks.

Journal of digital imaging
Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavior...

[Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disea...

Primer on machine learning: utilization of large data set analyses to individualize pain management.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both s...

The Use of Random Forests to Classify Amyloid Brain PET.

Clinical nuclear medicine
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.

Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain's anatomi...

Diagnostic and Prognostic Classification of Brain Disorders Using Residual Learning on Structural MRI Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this work, we study the potential of the deep residual neural network (ResNet) architecture to learn abstract neuroanatomical alterations in the structural MRI data by evaluating its diagnostic and prognostic classification performance on two larg...

Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Stroke is the fifth leading cause of death in the United States. Subarachnoid hemorrhage (SAH) is a type of stroke often caused by the spontaneous rupture of a cerebral aneurysm. About 30% of the SAH patients develop delayed cerebral ischemia (DCI) a...

[Artificial Intelligence for Diagnostic Support of Neuroimage].

Brain and nerve = Shinkei kenkyu no shinpo
Artificial intelligence (AI) shows promises in terms of diagnostic support on neuroimaging. We developed a software that predicts Alzheimer's disease (AD) using support vector machines (SVM) through three-dimensional brain MR images. Here, we will ex...