Neurology

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

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Annotation of epilepsy clinic letters for natural language processing.

BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured infor...

Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small panc...

Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks.

【PURPOSE】: Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis o...

The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characteriz...

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations,...

Automatic 3D pelvimetry framework in CT images and its validation.

In the field of spinal pathology, sagittal balance of the spine is usually judged by the spatial str...

An hetero-modal deep learning framework for medical image synthesis applied to contrast and non-contrast MRI.

Some pathologies such as cancer and dementia require multiple imaging modalities to fully diagnose a...

Prediction of Expanded Disability Status Scale in patients with MS using deep learning.

Multiple sclerosis (MS) is a chronic neurological condition that leads to significant disability in ...

Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.

Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinica...

Mild cognitive impairment prediction based on multi-stream convolutional neural networks.

BACKGROUND: Mild cognitive impairment (MCI) is the transition stage between the cognitive decline ex...

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques.

PURPOSE: The development of modern Artificial Intelligence (AI) based models for the early diagnosis...

Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network.

Estimating intracranial current sources underlying the electromagnetic signals observed from extracr...

Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices.

Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to e...

SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals.

Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional mach...

Machine learning-based classification of Parkinson's disease using acoustic features: Insights from multilingual speech tasks.

This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech charact...

Logistic regression analysis and machine learning for predicting post-stroke gait independence: a retrospective study.

This study investigated whether machine learning (ML) has better predictive accuracy than logistic r...

BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.

Decoding natural language from noninvasive brain signals has been an exciting topic with the potenti...

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