AIMC Topic: Neurology

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Machine learning in neurology: what neurologists can learn from machines and vice versa.

Journal of neurology
Artificial intelligence is increasingly becoming a part of everyday life. This raises the question whether clinical neurology can benefit from these novel methods to increase diagnostic accuracy. Several recent studies have used machine learning clas...

Inferring Clinical Correlations from EEG Reports with Deep Neural Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Successful diagnosis and management of neurological dysfunction relies on proper communication between the neurologist and the primary physician (or other specialists). Because this communication is documented within medical records, the ability to a...

Deep Learning in Neuroradiology.

AJNR. American journal of neuroradiology
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to ...

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

Computational and mathematical methods in medicine
High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional me...

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

Sensors (Basel, Switzerland)
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configur...

Neurodegenerative disease: MRI biomarkers - a precision medicine tool in neurology?

Nature reviews. Neurology
Two new studies highlight the potential of neuroimaging to aid the differential diagnosis of neurodegenerative disease, for both clinical practice and emerging trials. Although this approach holds great promise, meaningful implementation of neuroimag...

AI in Neurology: Everything, Everywhere, all at Once PART 2: Speech, Sentience, Scruples, and Service.

Annals of neurology
Artificial intelligence (AI) applications are finding use in real-world neurological settings. Whereas part 1 of this 3-part review series focused on the birth of AI and its foundational principles, this part 2 review shifts gears to explore more pra...

AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.

Annals of neurology
Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understa...

Neurologists and Clinical Informatics: Realizing the Potential of Digital Medicine.

Seminars in neurology
Clinical informatics (CI) is an emerging field within biomedical informatics that sits at the intersection of clinical care, health systems, and health information technology (IT). CI emphasizes how individuals (neurologists, patients, staff) interac...

Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome...