Neurology

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

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Plane coexistence behaviors for Hopfield neural network with two-memristor-interconnected neurons.

Memristors are commonly used as the connecting parts of neurons in brain-like neural networks. The m...

Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.

OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerativ...

Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcom...

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage cha...

Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.

This systematic literature review explores the intersection of neuroscience and deep learning in the...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hinder...

Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration.

The ever-increasing volume of complex data poses significant challenges to conventional sequential g...

Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.

Artificial intelligence (AI) systems are widely applied in various industries and everyday life, par...

Artificial Intelligence-Assisted Segmentation of a Falx Cerebri Calcification on Cone-Beam Computed Tomography: A Case Report.

Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic marke...

Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagi...

Effectiveness of robot-assisted training in adults with Parkinson's disease: a systematic review and meta-analysis.

AIM: This work aimed to update and summarize the existing evidence on the effectiveness of robot-ass...

Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms.

Alzheimer's disease (AD) is a serious neurodegenerative disorder that causes incurable and irreversi...

Early detection of Alzheimer's disease in structural and functional MRI.

OBJECTIVES: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net an...

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as in...

Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.

OBJECTIVE: The mandibular canal (MC) houses the inferior alveolar nerve. Extraction of the mandibula...

Distinguishing the activity of flexor digitorum brevis and soleus across standing postures with deep learning models.

BACKGROUND: Electromyographic (EMG) recordings indicate that both the flexor digitorum brevis and so...

Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning.

Segmentation in medical imaging is an essential and often preliminary task in the image processing c...

ChemNTP: Advanced Prediction of Neurotoxicity Targets for Environmental Chemicals Using a Siamese Neural Network.

Environmental chemicals can enter the human body through various exposure pathways, potentially lead...

Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.

With recent significant advancements in artificial intelligence, the necessity for more reliable rec...

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.

BACKGROUND: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in cl...

Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model.

The prediction of epileptic seizures is a classical research problem, representing one of the most c...

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