Neurology

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

OBJECTIVE: The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.

Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment.

The first ever insurance reimbursement for an artificial intelligence (AI) system, which expedites triage of acute stroke, occurred in 2020 when the Centers for Medicare and Medicaid Services (CMS) granted approval for a New Technology Add-on Payment (NTAP). Key aspects of the AI system that led to its approval by the CMS included its unique mechanism of action, use of robotic process automation, ...

Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a war...

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.

Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been use...

Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical la...

Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-deriv...

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional mo...

Deep learning for subtyping the Alzheimer's disease spectrum.

In a recent article from Cell Reports Medicine, Kwak et al. generate novel insights about subtyping cognitively impaired individuals based on structur...

The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review.

This systematic review aims to identify the available semi-automatic and fully automatic algorithms for inferior alveolar canal localization as well a...

Acute Effects of Nicotine on Physiological Responses and Sport Performance in Healthy Baseball Players.

There is interest in whether nicotine could enhance attention in sporting performance, but evidence on the acute effect of nicotine on physical respon...

[Artificial intelligence and glaucoma: A literature review].

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma...

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. R...

Big Data/AI in Neurocritical Care: Maybe/Summary.

Big data (BD) and artificial intelligence (AI) have increasingly been used in neurocritical care. "BD" can be operationally defined as extremely large...

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis.

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluations to ident...

Robotically-induced hallucination triggers subtle changes in brain network transitions.

The perception that someone is nearby, although nobody can be seen or heard, is called presence hallucination (PH). Being a frequent hallucination in ...

Is Blood Loss Greater in Elderly Patients under Antiplatelet or Anticoagulant Medication for Cervical Spine Injury Surgery? A Japanese Multicenter Survey.

INTRODUCTION: In elderly patients with cervical spinal cord injury, comorbidities such as cardiovascular and cerebrovascular diseases are common, with...

Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson's disease.

Parkinson's disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagno...

Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment.

BACKGROUND: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according ...

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

PURPOSE: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indic...

Combination of liquid crystal and deep learning reveals distinct signatures of Parkinson's disease-related wild-type α-synuclein and six pathogenic mutants.

α-Synuclein is a central player in Parkinson's disease (PD) pathology. Various point mutations in α-synuclein have been identified to alter the protei...