Latest AI and machine learning research in neurology for healthcare professionals.
Epilepsy affects approximately 50 million people worldwide, and seizures remain difficult to predict in onset, severity, and duration. Real-time seizure prediction may enable proactive intervention and improve patient safety and quality of life. Despite the development of high-performing algorithms, translation remains limited by predictive accuracy, interpretability, and generalisability. This sy...
Alzheimer’s disease (AD) involves early molecular changes beyond amyloid-β (Aβ) and tau, that create heterogeneous disease biology, giving rise to variable disease initiation and highly variable longitudinal trajectories. Accurately predicting trajectories is vital for design of clinical trials and for clinical care, yet current CSF and PET biomarkers provide limited predictive capabilities despit...
Music engages sensory, motor, cognitive, and emotional systems, making it a powerful model for studying experience-dependent neuroplasticity. Although...
Retinal fundus images offer a non-invasive window into systemic aging. Here, we fine-tuned a foundation model (RETFound) to predict chronological age ...
Early and accurate prediction of Alzheimer’s disease (AD) from accessible clinical data remains a significant challenge in healthcare. This study prop...
APOE-ε4, the strongest genetic risk factor for Alzheimer’s disease (AD), is linked to early motor vulnerability, including subtle speech control chang...
Nusinersen has substantially increased survival and improved disease progression in Spinal Muscular Atrophy (SMA) patients. However, treatment respons...
Cognitive decline affects up to 80% of Parkinson’s disease (PD) patients, significantly impacting quality of life. Speech changes occur early in PD an...
Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that primarily affects cognitive function. Early detection is a crucial factor in...
The goal of this study is seizure detection in four class datasets for different seizure stages in epileptic patients. An early notification system is...
Predicting the likelihood of developing Alzheimer’s disease (AD) dementia in at-risk individuals is important for the design of and optimal recruitmen...
Accurately distinguishing between epileptic seizures (ES) and nonepileptic seizures (NES) is a significant clinical challenge that typically requires ...
Adaptive deep brain stimulation has enabled machine learning models to track motor states from neural signals with improved accuracy, aiming to provid...
Accurate assessment of the severity of central canal stenosis (CCS) on lumbar spine MRI is critical for clinical decision-making. We evaluated deep le...
Clinical tests such as the Berg Balance Scale (BBS) and Timed Up and Go (TUG) are used to assess balance and functional mobility following stroke. The...
Immunotherapy has improved outcomes for advanced-stage melanoma, however, predictive biomarkers remain limited. We evaluated whether computed tomograp...
Clinicians currently lack reliable tools to determine, at the point of mild cognitive impairment (MCI), which individuals will progress to Alzheimer’s...
The Brain Imaging and Neurophysiology Database (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositorie...
Intracerebral hemorrhage (ICH) is among the most devastating forms of stroke, characterized by high early mortality and limited time-sensitive treatme...
Repetitive transcranial magnetic stimulation (rTMS) targeting the primary motor cortex (M1) provides significant pain relief in approximately 45% of p...