Neurology

Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis.

Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically small due to the considerable time, cost, and resources necessary to obtain them. A recently developed self-supervised learning method called Mixup may he...

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping...

Estimation of Cerebral Blood Flow and Arterial Transit Time From Multi-Delay Arterial Spin Labeling MRI Using a Simulation-Based Supervised Deep Neural Network.

BACKGROUND: An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time...

Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging.

Advances in deep learning can be applied to acute stroke imaging to build powerful and explainable prediction models that could supersede traditionall...

Three simple steps to improve the interpretability of EEG-SVM studies.

Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variet...

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular dise...

A deep learning-based approach to diagnose mild traumatic brain injury using audio classification.

Mild traumatic brain injury (mTBI or concussion) is receiving increased attention due to the incidence in contact sports and limitations with subjecti...

A coarse-to-fine cascade deep learning neural network for segmenting cerebral aneurysms in time-of-flight magnetic resonance angiography.

BACKGROUND: Accurate segmentation of unruptured cerebral aneurysms (UCAs) is essential to treatment planning and rupture risk assessment. Currently, t...

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.

BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predic...

Predictive Model for Human Activity Recognition Based on Machine Learning and Feature Selection Techniques.

Research into assisted living environments -within the area of Ambient Assisted Living (ALL)-focuses on generating innovative technology, products, an...

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease.

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medicatio...

Application of supervised machine learning algorithms to predict the risk of hidden blood loss during the perioperative period in thoracolumbar burst fracture patients complicated with neurological compromise.

BACKGROUND: Machine learning (ML) is a type of artificial intelligence (AI) and has been utilized in clinical research and practice to construct high-...

A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet.

Ultrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morpholog...

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings co...

Revisiting dose and intensity of training: Opportunities to enhance recovery following stroke.

PURPOSE: Stroke is a global leading cause of adult disability with survivors often enduring persistent impairments and loss of function. Both intensit...

Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush inj...

Imaging of the optic nerve: technological advances and future prospects.

Over the past decade, ocular imaging strategies have greatly advanced the diagnosis and follow-up of patients with optic neuropathies. Developments in...

Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review.

This review aims to recommend directions for future research on robotic biofeedback towards prompt post-stroke gait rehabilitation by investigating th...

Interpretable deep learning of myelin histopathology in age-related cognitive impairment.

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aβ) plays...

Artificial intelligence (AI) for home support interventions in dementia: a scoping review protocol.

INTRODUCTION: Dementia has become one of the significant causes of disability and dependency among older people globally. The proportion of people wit...