Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Advanced rehabilitation in ischaemic stroke research.

At present, due to the rapid progress of treatment technology in the acute phase of ischaemic stroke...

Robot-assisted gait training in patients with various neurological diseases: A mixed methods feasibility study.

BACKGROUND: Walking impairment represents a relevant symptom in patients with neurological diseases ...

Development of an artificial intelligence-based model to predict early recurrence of neuroendocrine liver metastasis after resection.

PURPOSE: We sought to develop an artificial intelligence (AI)-based model to predict early recurrenc...

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.

BACKGROUND: The objective of this study was to establish a predictive model utilizing machine learni...

Digital health in stroke: a narrative review.

Digital health is significantly transforming stroke care, particularly in remote and economically di...

Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.

Alzheimer's disease (AD) involves aggregation of amyloid β and tau, neuron loss, cognitive decline, ...

Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.

Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applicat...

Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

OBJECTIVES: To investigate whether machine learning (ML)-based center of pressure (COP) analysis for...

Machine learning for (non-)epileptic tissue detection from the intraoperative electrocorticogram.

OBJECTIVE: Clinical visual intraoperative electrocorticography (ioECoG) reading intends to localize ...

Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning.

BACKGROUND:  Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebr...

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a va...

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detect...

Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where ac...

Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.

Notification systems that convey urgency without adding cognitive burden are crucial in human-comput...

The use of artificial neural networks in studying the progression of glaucoma.

In ophthalmology, artificial intelligence methods show great promise due to their potential to enhan...

Neuromorphic learning and recognition in WOthin film-based forming-free flexible electronic synapses.

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of ...

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) ca...

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