Neurology

Evaluation of the mandibular canal and the third mandibular molar relationship by CBCT with a deep learning approach.

OBJECTIVE: The mandibular canal (MC) houses the inferior alveolar nerve. Extraction of the mandibular third molar (MM3) is a common dental surgery, often complicated by nerve damage. CBCT is the most effective imaging method to assess the relationship between MM3 and MC. With advancements in artificial intelligence, deep learning has shown promising results in dentistry. The aim of this study is t...

Distinguishing the activity of flexor digitorum brevis and soleus across standing postures with deep learning models.

BACKGROUND: Electromyographic (EMG) recordings indicate that both the flexor digitorum brevis and soleus muscles contribute significantly to the control of standing balance, However, less is known about the adjustments in EMG activity of these two muscles across different postures.

Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised learning.

Segmentation in medical imaging is an essential and often preliminary task in the image processing chain, driving numerous efforts towards the design ...

Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.

BACKGROUND: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.

Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model.

The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorder...

Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.

BACKGROUND: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning...

Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.

With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguar...

Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.

BACKGROUND: Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementa...

MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers.

BACKGROUND: Quantitative magnetic resonance imaging (MRI) analysis has shown promise in differentiating neurodegenerative Parkinsonian syndromes and h...

Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.

Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as...

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. ...

Artificial intelligence terminology, methodology, and critical appraisal: A primer for headache clinicians and researchers.

OBJECTIVE: The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicia...

3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.

Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, ...

Natural compounds for Alzheimer's prevention and treatment: Integrating SELFormer-based computational screening with experimental validation.

BACKGROUND: This study aimed to develop and apply a novel computational pipeline combining SELFormer, a transformer architecture-based chemical langua...

Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WM...

Influence of renal function on blood pressure control and outcome in thrombolyzed patients after acute ischemic stroke: analysis of the ENCHANTED trial.

BACKGROUND: The effect of renal impairment in patients who receive intravenous thrombolysis for acute ischemic stroke (AIS) is unclear. We aimed to de...

Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.

BACKGROUND AND AIM: Neurodegenerative disorders (e.g., Alzheimer's, Parkinson's) lead to neuronal loss; neurocognitive disorders (e.g., delirium, deme...

Neurodegeneration: 2024 update.

This review highlights a collection of both diverse and highly impactful studies published in the previous year selected by the author from the neurod...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worl...

An assessment of the influence of trade-off optimization in commercial knowledge based planning library creation for tongue cancer patients.

This article aims to compare the dosimetric performance between knowledge-based plan (KBP) libraries with and without trade-off (TO) exploration using...