Latest AI and machine learning research in neurology for healthcare professionals.
Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to specialized diagnostic tests, and overreliance on single-type indicators. These challenges are exacerbated by the progressive nature of AD, where subtle pathophysiological changes often precede clinical symptoms by decades. To address these limitations...
Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, especially with diverse patient populations. This study developed a deep learning-based graph neural network (GNN) model to predict seizure freedom from stereo electroencephalography (sEEG) data in patients with refractory epilepsy. We utilized high-qual...
This study introduces a specialized pipeline designed to classify the concentration state of an individual student during online learning sessions b...
Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditiona...
Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skele...
This study examines the potential causal relationship between head injury and the risk of developing Alzheimer's disease (AD) using Bayesian network...
Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding, en...
Accurate prediction of CB2 receptor ligand activity is pivotal for advancing drug discovery targeting this receptor, which is implicated in inflamma...
We present a hybrid brain-machine interface (BMI) that integrates steady-state visually evoked potential (SSVEP)-based EEG and facial EMG to improve...
This study presents the development and testing of a conversational speech system designed for robots to detect speech biomarkers indicative of cogn...
The prevalence of hearing aids is increasing. However, optimizing the amplification processes of hearing aids remains challenging due to the complex...
The rapid emergence of highly adaptable and reusable artificial intelligence (AI) models is set to revolutionize the medical field, particularly in ...
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. Thi...
Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. ...
The early diagnosis of Alzheimer's Disease (AD) through non invasive methods remains a significant healthcare challenge. We present NeuroXVocal, a n...
Background: Computed Tomography Angiography (CTA) is crucial for cerebrovascular disease diagnosis. Dynamic CTA is a type of imaging that captures t...
Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research...
The differential diagnosis of neurodegenerative diseases, characterized by overlapping symptoms, may be challenging. Brain imaging coupled with arti...
In remote healthcare monitoring, time series representation learning reveals critical patient behavior patterns from high-frequency data. This study...
To create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different condit...