Latest AI and machine learning research in neurology for healthcare professionals.
With all the advances in both the science of aging and artificial intelligence (AI), we are in a propitious position to accurately and precisely determine who is at high risk of developing Alzheimer's disease years before signs of even mild cognitive deficit. It takes at least 20 years for aggregates of misfolded β-amyloid and tau proteins to accumulate in the brain along with neuroinflammation th...
Wearable technology, combined with artificial intelligence (AI) and machine learning (ML) algorithms, opens up new frontiers for continuously monitoring physiological or behavioural data, allowing the identification of stroke risk factors at an earlier stage. A systematic search was performed in PubMed, IEEE Xplore, Scopus, Google Scholar, Cochrane Library, and Web of Science, following the PRISMA...
Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representa...
Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilit...
Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imb...
Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imb...
Alzheimer's detection efforts aim to develop accurate models for early disease diagnosis. Significant advances have been achieved with convolutional...
Over the past decade, considerable research has been directed towards assistive technologies to support people with vision impairments using machine...
Data augmentation has been demonstrated to improve the classification accuracy of deep learning models in steady-state visual evoked potential-based b...
Reconstructing and understanding dynamic visual information (video) from brain EEG recordings is challenging due to the non-stationary nature of EEG...
The analysis of speech in individuals with amyotrophic lateral sclerosis is a powerful tool to support clinicians in the assessment of bulbar dysfun...
Retinal imaging has emerged as a powerful, non-invasive modality for detecting and quantifying biomarkers of systemic diseases-ranging from diabetes...
Alzheimer's disease (AD) is the most prevalent type of dementia. It is linked with a gradual decline in various brain functions, such as memory. Many ...
The differential diagnosis of neurodegenerative dementias is a challenging clinical task, mainly because of the overlap in symptom presentation and ...
Human preference research is a significant domain in psychology and psychophysiology, with broad applications in psychiatric evaluation and daily li...
BACKGROUND: This paper presents a deep learning model for the automated segmentation of cerebral cavernous malformations (CCMs).
Whisper fails to correctly transcribe dementia speech because persons with dementia (PwDs) often exhibit irregular speech patterns and disfluencies ...
Brain aging trajectories differ between males and females, yet the genetic factors underlying these differences remain underexplored. Using structur...
The holistic philosophy of traditional Chinese medicine (TCM) embodied in acupuncture therapy has gained novel insights within the neuroimmune regulat...
Parkinson's Disease (PD) is a neurodegenerative disorder that significantly impacts motor and non-motor functions. There is currently no treatment t...