Neurology

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healt...

Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment.

Mild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, that is beyond what is expected due to normal aging. Detection of MCI is crucial for providing appropriate interventions and slowing down the progression of dementia. There are several automated predictive algorithms for prediction using time-to-event d...

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding n...

Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.

Astrocytes, a type of glial cell, significantly influence neuronal function, with variations in morphology and density linked to neurological disorder...

Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only.

Although studies on terrain identification algorithms to control walking assistive devices have been conducted using sensor fusion, studies on transit...

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powe...

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a r...

Serum Lidocaine Levels in Adult Patients Undergoing Cardiac Surgery With del Nido Cardioplegia.

BACKGROUND: Lidocaine in del Nido cardioplegia solution prolongs the refractory period of cardiomyocytes, yielding a longer arrest per dose. Serum lid...

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily ...

Validation of reliability, repeatability and consistency of three-dimensional choroidal vascular index.

This study aimed to investigate the reliability, repeatability and consistency of choroidal vascularity index (CVI) measurements provided by an artifi...

A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and i...

Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis.

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of m...

Artificial intelligence-based decision support software to improve the efficacy of acute stroke pathway in the NHS: an observational study.

INTRODUCTION: In a drip-and-ship model for endovascular thrombectomy (EVT), early identification of large vessel occlusion (LVO) and timely referral t...

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innov...

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

OBJECTIVE: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke ...

Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.

Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, howev...

Effectiveness and safety of robot-assisted minimally invasive transforaminal lumbar interbody fusion for degenerative lumbar spinal diseases: a systematic review and meta-analysis.

Robot-assisted (RA) technology has been widely used in spine surgery. This analysis aimed to compare the effectiveness and safety of RA minimally inva...

Global brain analysis of minor hallucinations in Parkinson's disease using EEG and MRI data.

INTRODUCTION: Visual hallucination is a prevalent psychiatric disorder characterized by the occurrence of false visual perceptions due to misinterpret...

User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment.

User identification systems based on electromyogram (EMG) signals, generated inside the body in different signal patterns and exhibiting individual ch...

Dual statistical models link baseline visual attention measure to risk for significant symptomatic concussion in sports.

AIM: Athletic pre-season testing can establish functional baseline for comparison following concussion. Whether impacts of future concussions may be f...