Latest AI and machine learning research in neurology for healthcare professionals.
Drug-resistant epilepsy (DRE) affects over 50 million individuals worldwide, yet surgical resection, the most effective treatment, achieves seizure freedom in only approximately 50% of cases. A reliable, noninvasive method for preoperative surgical outcome prediction is therefore critically needed to avoid the risks of invasive intracranial monitoring. This retrospective study developed a fully no...
BACKGROUND: Alzheimer's Disease (AD) is a complex neurodegenerative disorder, with women comprising nearly two-thirds of individuals with AD. However, sex-specific heterogeneity in AD progression remains insufficiently understood. A data-driven approach is needed to characterise such heterogeneity from longitudinal electronic health records (EHRs). METHODS: We developed a deep learning-based frame...
INTRODUCTION: Accurate localization of the mandibular canal in Cone-Beam Computed Tomography (CBCT) images is critical for preventing iatrogenic nerve...
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder marked by progressive cognitive decline, yet its transcriptional regulatory ar...
Trigeminal neuralgia (TN) is a debilitating neuropathic pain disorder characterized by sudden, intense facial pain, with diagnosis heavily reliant on ...
Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) poses significant challenges in therapeutic management. While studies have demonstrated sei...
BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assist...
PURPOSE: The advancement of decision support systems for pathology and their implementation in clinical practice have been limited by the necessity fo...
The dual activating potential of peroxisome proliferator-activated receptors (PPAR) α and γ offers a promising way to address metabolic issues, neuroi...
OBJECTIVES/BACKGROUND: Migraine is a disabling neurological disorder with substantial interindividual variability. Predicting whether an ongoing migra...
Alzheimer's disease (AD) is a common neurodegenerative disorder. Compared to the limited specificity of single-biomarker detection, the combined detec...
BACKGROUND: The Fazekas score is widely used to grade white matter hyperintensities (WMHs) in cerebral small vessel disease, yet the equivalent volume...
BackgroundDigital speech analysis affords robust markers of Parkinson's disease (PD). However, most studies target late-onset PD (LOPD), neglecting ea...
Brain age estimation using machine learning has gained significant attention as a promising approach to assess cognitive health and aging. By analyzin...
Lysine β-hydroxybutyrylation (Kbhb) is an emerging post-translational modification regulated by β-hydroxybutyrate (BHB), a key metabolic intermediate ...
Accurate recognition of human emotions from electroencephalogram (EEG) signals is fundamental to affective computing, yet it remains challenging due t...
OBJECTIVES: Great saphenous vein (GSV) incompetence is common, but numerous treatment options complicate patient-treatment matching. This narrative re...
Electroencephalography (EEG)-based motor imagery classification plays an important role in brain-computer interface (BCI) systems. However, existing m...
Cognitive impairment is an important constraint for PwPD with Parkinson's disease (PwPD). Digital assessments potentially provide more accessible meas...
Autism Spectrum Disorder (ASD) diagnosis benefits from the technical analysis of neural oscillations. The objective identification of Autism Spectrum ...