Neurology

Artificial intelligence for dementia-Applied models and digital health.

INTRODUCTION: The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

Rapid eye movement sleep loss associated cytomorphometric changes and neurodegeneration.

Rapid eye movement sleep (REMS) is essential for leading normal healthy living at least in higher-order mammals, including humans. In this review, we briefly survey the available literature for evidence linking cytomorphometric changes in the brain due to loss of REMS. As a mechanism of action, we add evidence that REMS loss elevates noradrenaline (NA) levels in the brain, which affects neuronal c...

Left recurrent nerve lymph node dissection in robotic esophagectomy for esophageal cancer without esophageal traction.

BACKGROUND: Because the robotic arm is located on the dorsal side of the patient, when the esophagus is pulled dorsally for the left recurrent nerve l...

Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond.

Machine learning on electromyography (EMG) has recently achieved remarkable success on various tasks, while such success relies heavily on the assumpt...

A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques.

The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively ...

ChatGPT vs Google for Queries Related to Dementia and Other Cognitive Decline: Comparison of Results.

BACKGROUND: People living with dementia or other cognitive decline and their caregivers (PLWD) increasingly rely on the web to find information about ...

Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.

BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource manag...

Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.

OBJECTIVES: This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods.

Novel use of robot-assisted gait rehabilitation in a patient with stroke and blindness.

Robot-assisted gait training (RAGT) is an effective adjunctive treatment for patients with stroke that helps to regain functional mobility and is appl...

Comparing the Lower-Limb Muscle Activation Patterns of Simulated Walking Using an End-Effector-Type Robot with Real Level and Stair Walking in Children with Spastic Bilateral Cerebral Palsy.

Cerebral palsy is a neurologic disorder caused by lesions on an immature brain, often resulting in spasticity and gait abnormality. This study aimed t...

Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

PRCIS: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced th...

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammatory disorders of the central nervous system (CNS) w...

Probing the molecular mechanisms of α-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations.

Parkinson's disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumula...

Pedicle Screw Placement Accuracy in Robot-Assisted Spinal Fusion in a Multicenter Study.

Pedicle screw fixation is a spinal fusion technique that involves the implantation of screws into vertebral pedicles to restrict movement between thos...

Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method i...

Rational design of functional amyloid fibrillar assemblies.

Amyloid fibrillar assemblies, originally identified as pathological entities in neurodegenerative diseases, have been widely adopted by various protei...

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.

Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to val...

Towards Precision Medicine in Spinal Surgery: Leveraging AI Technologies.

This critique explores the implications of integrating artificial intelligence (AI) technology, specifically OpenAI's advanced language model GPT-4 an...

Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems.

Electroencephalograms (EEG) is used to assess patients' clinical records of depression (EEG). The disorder of human thinking is a very complex problem...

Initial Testing of Robotic Exoskeleton Hand Device for Stroke Rehabilitation.

The preliminary test results of a novel robotic hand rehabilitation device aimed at treatment for the loss of motor abilities in the fingers and thumb...