Neurology

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

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Deep Learning-Based Diagnostic Model for Parkinson's Disease Using Handwritten Spiral and Wave Images.

OBJECTIVE: To develop and validate a deep neural network (DNN) model for diagnosing Parkinson's Dise...

Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers.

PURPOSE: This study explores the application of machine learning to high-dimensional proteomics data...

SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.

Despite the vast number of publications reporting seizures and the reliance of the field on accurate...

Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility.

PURPOSE: Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoper...

Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.

Machine learning approaches including deep learning models have shown promising performance in the a...

Machine-learning models for the prediction of ideal surgical outcomes in patients with adult spinal deformity.

AIMS: Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgi...

Denoising Diffusion Probabilistic Model to Simulate Contrast-enhanced spinal MRI of Spinal Tumors: A Multi-Center Study.

RATIONALE AND OBJECTIVES: To generate virtual T1 contrast-enhanced (T1CE) sequences from plain spina...

Deep Learning-Assisted Diagnosis of Malignant Cerebral Edema Following Endovascular Thrombectomy.

BACKGROUND: Malignant cerebral edema (MCE) is a significant complication following endovascular thro...

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours.

BACKGROUND: Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, ...

The Role of AI in the Evaluation of Neuroendocrine Tumors: Current State of the Art.

Advancements in Artificial Intelligence (AI) are driving a paradigm shift in the field of medical di...

A hybrid network based on multi-scale convolutional neural network and bidirectional gated recurrent unit for EEG denoising.

Electroencephalogram (EEG) signals are time series data containing abundant brain information. Howev...

Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.

BACKGROUND: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, pa...

Machine learning analysis of cortical activity in visual associative learning tasks with differing stimulus complexity.

Associative learning tests are cognitive assessments that evaluate the ability of individuals to lea...

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. Thi...

Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration an...

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