Neurology

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learni...

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications.

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing ...

Lower, Variable Intrathecal Opioid Doses, and the Incidence of Prolonged Fetal Heart Rate Decelerations After Combined Spinal Epidural Analgesia for Labor: A Quality Improvement Analysis.

BACKGROUND: Combined spinal-epidurals with low-dose intrathecal opioids and local anesthetics are commonly used to initiate labor analgesia due to the...

Machine learning-based multimodal prediction of language outcomes in chronic aphasia.

Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following th...

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from ...

Development of Inappropriate Vasopressin Secretion in Association With Lumbar Cerebrospinal Fluid Drainage in an Adult With Traumatic Basilar Skull Fracture.

OBJECTIVE: Hyponatremia associated with cerebrospinal fluid (CSF) drainage is primarily limited to pediatric patients. Only 1 case in an adult, after ...

Deep learning detection of informative features in tau PET for Alzheimer's disease classification.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive dec...

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinic...

Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.

In recent years, deep learning has emerged as a powerful tool for developing Brain-Computer Interface (BCI) systems. However, for deep learning models...

Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity.

Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. De...

Neural Interactions in Developing Rhythmogenic Spinal Networks: Insights From Computational Modeling.

The mechanisms involved in generation of rhythmic locomotor activity in the mammalian spinal cord remain poorly understood. These mechanisms supposedl...

Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery.

OBJECTIVE: We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE...

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.

BACKGROUND: Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint.

Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment.

Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. To understand the brain functions and identify t...

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received ...

Comprehensive study on applications of artificial neural network in food process modeling.

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to...

Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports.

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the chan...

Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging.

The classification of Alzheimer's disease (AD) using deep learning methods has shown promising results, but successful application in clinical setting...

InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across sub...

Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.

The impact of sex and gender in the incidence and severity of coronavirus disease 2019 (COVID-19) remains controversial. Here, we aim to describe the...