Latest AI and machine learning research in neurology for healthcare professionals.
This paper presents the Neurobiome Navigator, an AI-powered, highly interactive, and easily navigable application designed to help users explore the complex relationships between the human microbiome and Parkinson’s disease (PD). The app focuses on the gut microbiome and the oral microbiome, known to have a strong relationship with PD, as well as impulse control disorders (ICD), a significant non-...
Recovery from aphasia after stroke is thought to depend on functional reorganization of language processing in surviving brain regions. Many studies have investigated this process, but progress has been impeded by methodological limitations relating to task performance confounds, contrast validity, and sample sizes. Furthermore, few studies have accounted for the complex relationships that exist b...
Acute ischemic stroke (AIS) management has evolved substantially over the past two decades, with mechanical thrombectomy adding complexity that requir...
We thoroughly investigated the generalizability of deep learning models trained on electroencephalography (EEG) data to detect Alzheimer’s disease and...
Current radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particular...
DNA foundation models offer a new approach to interpret genetic variation, but their potential in population-scale genomics remains untapped. We intro...
Ischemic stroke in young adults is a significant social and economic burden. Machine learning (ML) techniques can potentially predict the outcomes of ...
The indicator cell assay platform (iCAP) is a novel next-generation approach for blood-based diagnostics that uses standardized cells as biosensors to...
Sleep disturbances and altered circadian rhythms are well-documented in both physiological and biological studies of dementia. The exact causal relati...
Acute stroke alerts are often activated for non-cerebrovascular conditions, leading to false positives that strain clinical resources and promote diag...
Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To enhance the identifica...
Automated interpretation of resting-state fMRI (rs-fMRI) for epilepsy diagnosis remains a challenge. We developed a regularized transformer that model...
Self-supervised pre-trained speech models such as wav2vec 2.0 provide rich frame-level embeddings that are increasingly used for clinical voice screen...
Epilepsy affects approximately 1% of the global population, with 30-40% of cases resistant to conventional pharmacological treatments. Current neurost...
To test the hypothesis that deep learning can decode single-trial cortical responses from electroencephalography (EEG) to individual, long-duration mi...
Identifying MS in children early and distinguishing it from other neuroinflammatory conditions of childhood is critical, as early therapeutic interven...
Explainable Artificial Intelligence (XAI) methods enhance the diagnostic efficiency of clinical decision support systems by making the predictions of ...
Automated thalamic nuclear segmentation has contributed towards a shift in neuroimaging analyses from treating the thalamus as a homogeneous, passive ...
Musculoskeletal (MSK) modeling and ultrasound imaging (USI) are complementary techniques that, when combined with three-dimensional gait analysis (3DG...
Disrupted brain iron metabolism and activated ferroptosis during ageing constitute significant precursors to neurodegenerative diseases. However, whet...