Neurology

Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.

BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications are equally crucial. This study aims to develop an interpretable machine learning (IML) model that effectively predicts in-hospital mortality for ischem...

Development and validation of radiomics and deep transfer learning models to assess cognitive impairment in patients with cerebral small vessel disease.

Cognitive impairment in cerebral small vessel disease (CSVD) progresses subtly but carries significant clinical consequences, necessitating effective diagnostic tools. This study developed and validated predictive models for CSVD-related cognitive impairment using deep transfer learning (DTL) and radiomics features extracted from hippocampal 3D T1-weighted MRI. A total of 145 CSVD patients and 99 ...

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth fa...

Rapid Blood Clot Removal via Remote Delamination and Magnetization of Clot Debris.

Micro/nano-scale robotic devices are emerging as a cutting-edge approach for precision intravascular therapies, offering the potential for highly targ...

Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.

BACKGROUND: Integrating machine learning with medical records offers potential for early detection of Alzheimer's disease (AD), enabling timely interv...

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is...

MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer's disease diagnosis.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high inc...

Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs.

BACKGROUND: Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the...

Outcomes of lateral femoral cutaneous nerve decompression surgery in meralgia paraesthetica: assessment of pain, sensory deficits, and quality of life.

PURPOSE: Meralgia paraesthetica (MP) is a rare neuropathy of the lateral femoral cutaneous nerve (LFCN), characterized by thigh pain, paraesthesia, or...

Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.

Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause variou...

Rehabilitation training robot using mirror therapy for the upper and lower limb after stroke: a prospective cohort study.

BACKGROUND: This prospective cohort study was designed to investigate and compare the effectiveness of rehabilitation training robots versus conventio...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvem...

Dynamic Prediction and Intervention of Serum Sodium in Patients with Stroke Based on Attention Mechanism Model.

Abnormal serum sodium levels are a common and severe complication in stroke patients, significantly increasing mortality risk and prolonging ICU stays...

Alzheimer's disease prediction using 3D-CNNs: Intelligent processing of neuroimaging data.

Alzheimer's disease (AD) is a severe neurological illness that demolishes memory and brain functioning. This disease affects an individual's capacity ...

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals.

Low Back Pain (LBP) is the most prevalent musculoskeletal condition worldwide and a leading cause of disability, significantly affecting mobility, wor...

Predicting EEG seizures using graded spiking neural networks.

To develop and evaluate a novel, non-patient-specific epileptic seizure prediction system using graded spiking neural networks (GSNNs) implemented on ...

Deep learning models as learners for EEG-based functional brain networks.

Functional brain network (FBN) methods are commonly integrated with deep learning (DL) models for EEG analysis. Typically, an FBN is constructed to ex...

Deep learning-based classification of dementia using image representation of subcortical signals.

BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common...

How Can Robotic Devices Help Clinicians Determine the Treatment Dose for Post-Stroke Arm Paresis?

Upper limb training dose after stroke is usually quantified by time and repetitions. This study analyzed upper limb motor training dose in stroke part...