Neurology

Automated posture adjustment system for immobilized patients using EEG signals.

This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-pr...

Artificial intelligence-enhanced pain management: the NXTSTIM EcoAI platform.

Chronic pain affects approximately 20% of the global population, leading to significant disability and economic burden. Traditional management strategies, including pharmacologic interventions and physical therapies, often provide limited relief and are associated with adverse effects. Non-invasive neuromodulation techniques, such as transcutaneous electrical nerve stimulation (TENS) and electrica...

Orbital CT deep learning models in thyroid eye disease rival medical specialists' performance in optic neuropathy prediction in a quaternary referral center and revealed impact of the bony walls.

PURPOSE: To develop and evaluate orbital CT deep learning (DL) models in optic neuropathy (ON) prediction in patients diagnosed with thyroid eye disea...

Developments in MRI radiomics research for vascular cognitive impairment.

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following p...

Ultradeep N-glycoproteome atlas of mouse reveals spatiotemporal signatures of brain aging and neurodegenerative diseases.

The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achi...

Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy.

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are a growing global health concern, especially am...

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical...

FSID: a novel approach to human activity recognition using few-shot weight imprinting.

Accurate recognition of human activities from gait sensory data plays a vital role in healthcare and wellness monitoring. However, conventional deep l...

Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis.

Parkinson's Disease (PD) is a deteriorating condition that mostly affects older people. The lack of conclusive treatment for PD makes diagnosis very c...

STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG.

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patien...

Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.

Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and act...

A modified generative adversarial networks method for assisting the diagnosis of deep venous thrombosis complications in stroke patients.

Discriminate deep vein thrombosis, one of the complications in early stroke patients, in order to assist in diagnosis. We have constructed a new metho...

Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.

The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impre...

Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.

Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate dia...

Proteomic risk scores for predicting common diseases using linear and neural network models in the UK biobank.

Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological ...

Improving EEG based brain computer interface emotion detection with EKO ALSTM model.

Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered comp...

EmoTrans attention based emotion recognition using EEG signals and facial analysis with expert validation.

Emotion recognition via EEG signals and facial analysis becomes one of the key aspects of human-computer interaction and affective computing, enabling...

Machine learning of time series data using persistent homology.

This study proposes a novel method for time-series analysis based on persistent homology. Traditional time-series analysis techniques based on persist...

Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach.

Diabetic foot Ulceration (DFU) is a severe complication of diabetic foot syndrome, often leading to amputation. In patients with neuropathy, ulcer for...