Latest AI and machine learning research in neurology for healthcare professionals.
Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of these diseases and what care dementia patients receive in their final years of life. Using a large volume of clinical notes of dementia patients over the last two years of life, we conducted automatic topic modeling to capture the trends of various theme...
BACKGROUND: As of 2014, stroke is the fourth leading cause of death in Japan. Predicting a future diagnosis of stroke would better enable proactive forms of healthcare measures to be taken. We aim to predict a diagnosis of stroke within one year of the patient's last set of exam results or medical diagnoses.
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task...
Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and dis...
Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency...
BACKGROUND: Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multip...
Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We arg...
BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic chan...
In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and progn...
BACKGROUND: Lymphomatosis cerebri (LC) is a unique form of primary central nerves lymphoma (PCNSL), which presents as diffuse infiltration of lymphoma...
OBJECTIVES: Nerve-sparing radical hysterectomy has been implemented in order to reduce pelvic floor dysfunctions in women undergoing radical surgery f...
The review aims at providing current state of evidence in the field of medicine with fuzzy logic for diagnosing diseases. Literature reveals that fuzz...
OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this techniq...
BACKGROUND AND PURPOSE: Pulmonary function testing is a standard part of care for patients admitted to hospital with a myasthenia gravis exacerbation....
Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in bot...
BACKGROUND AND OBJECTIVE: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and imp...
End-effector-based robotic systems are, in particular, suitable for extending physical therapy in stroke rehabilitation. An adequate therapy and thus ...
Machine learning algorithms that use data streams captured from soft wearable sensors have the potential to automatically detect PD symptoms and infor...
BACKGROUND: Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow res...
Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to seve...