Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Retrospect and prospect: a visual analysis of artificial intelligence applications in neurorehabilitation

To explore the current research status and future development trend of artificial intelligence in neurorehabilitation. To collect and organize the literature on AI in neurorehabilitation from the core collection of Web of Science (WOS) database in the past ten years, and summarize the current status and predict the future development trend of AI in neurorehabilitation by using VOSviewer software a...

Clinician-Led Code-Free Deep Learning for Detecting Papilloedema and Pseudopapilloedema Using Optic Disc Imaging

Differentiating pseudopapilloedema from papilloedema is challenging, but critical for prompt diagnosis and to avoid unnecessary invasive procedures. Following diagnosis of papilloedema, objectively grading severity is important for determining urgency of management and therapeutic response. Automated machine learning (AutoML) has emerged as a promising tool for diagnosis in medical imaging and may...

DWI and Clinical Characteristics Correlations in Acute Ischemic Stroke After Thrombolysis

Magnetic Resonance Diffusion-Weighted Imaging (DWI) is a crucial tool for diagnosing acute ischemic stroke, yet some patients present as DWI-negative....

A systems immunology analysis of Alzheimer’s disease reveals an age- and environmental exposure-independent disturbance in B cell maturation

Alzheimer’s disease is a severe neurodegenerative disorder, with multifactorial mechanisms of disease development and progression. Evidence from genet...

Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development

Myasthenia gravis (MG) is a rare autoimmune neuromuscular disease. Clinical trials with rigorously collected data, especially for rare diseases, provi...

Prediction of impulse control disorders in Parkinson’s disease: a longitudinal machine learning study

Impulse control disorders (ICD) in Parkinson’s disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several ...

Completeness and Quality of Neurology Referral Letters Generated by a Large Language Model for Standardized Scenarios

Large Language Models (LLMs) offer promising applications in healthcare, including drafting referral letters. However, access to LLMs specifically des...

Multi-Orientation Hippocampus-Centered 3D CNN with Attention Mechanism for Alzheimer’s Disease Classification from MRI Scans

Alzheimer’s disease detection faces challenges in capturing hippocampal atrophy across multiple anatomical orientations. This study presents a multi-o...

Cascaded Multimodal Deep Learning in the Differential Diagnosis, Progression Prediction, and Staging of Alzheimer’s and Frontotemporal Dementia

Dementia is a complex condition whose multifaceted nature poses significant challenges in the diagnosis, prognosis, and treatment of patients. Despite...

Event-based seizure detection in human iEEG with neuromorphic hardware

Epilepsy is a neurological disorder that affects approximately 1% of the global population. The current method for seizure monitoring, seizure diaries...

CharMark: A Markov Approach to Linguistic Biomarkers in Dementia

Dementia, one of the most prevalent neurodegenerative diseases, affects millions worldwide. Understanding linguistic markers of dementia is crucial fo...

Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investiga...

A 60-Second Interpretable Voice Model for Early Dementia Screening

Early detection of cognitive impairment in assisted living is hindered by time-intensive tools like MMSE and MoCA. We present a 60-second voice-based ...

Large Language Models in Stroke Management: A Review of the Literature

Stroke care generates vast free-text records that slow chart review and hamper data reuse. Large language models (LLMs) have been trialed as a remedy ...

Multimodal Integration of Alzheimer’s Plasma Biomarkers, MRI, and Genetic Risk for Individual Prediction of Cerebral Amyloid Burden

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ...

Neuroinflammation distinguishes HLA haplotypes in progressive supranuclear palsy

Progressive supranuclear palsy (PSP) is a neurodegenerative 4R tauopathy clinically presenting with atypical parkinsonism or cognitive behavioral chan...

Multivariate whole brain neurodegenerative-cognitive-clinical severity mapping in the Alzheimer’s disease continuum using explainable AI

Neurodegeneration and cognitive impairment are commonly reported in Alzheimer’s disease (AD); however, their multivariate links are not well understoo...

SLOTMFound: Foundation-Based Diagnosis of Multiple Sclerosis Using Retinal SLO Imaging and OCT Thickness-maps

Multiple Sclerosis (MS) is a chronic autoimmune disorder of the central nervous system that can lead to significant neurological disability. Retinal i...

Patient-Specific and Interpretable Deep Brain Stimulation Optimisation Using MRI and Clinical Review Data

Optimisation of Deep Brain Stimulation (DBS) settings is a key aspect in achieving clinical efficacy in movement disorders, such as the Parkinson’s di...

Improving Responsiveness in Game-based Cognitive Assessment for Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) affects up to 20% of older adults and often progresses to dementia. While brief cognitive screening tools like the Mon...

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