Neurology

Deep learning in optical coherence tomography: Where are the gaps?

Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect th...

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering f...

Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.

It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective...

Implantation accuracy and operative variables in robot-assisted stereoelectroencephalography.

OBJECTIVE: The stereoelectroencephalography (SEEG) procedure provides a unique 3D overview of the seizure-onset zone. Although the success of SEEG rel...

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning.

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organizati...

Exoskeleton Training Modulates Complexity in Movement Patterns and Cortical Activity in Able-Bodied Volunteers.

Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot intera...

Multi-Modal Deep Learning Diagnosis of Parkinson's Disease-A Systematic Review.

Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, ...

Deep Learning Aided Neuroimaging and Brain Regulation.

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuro...

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative dise...

The effectiveness of a therapeutic robot, 'Paro', on behavioural and psychological symptoms, medication use, total sleep time and sociability in older adults with dementia: A systematic review and meta-analysis.

OBJECTIVE: To evaluate the effectiveness of a therapeutic robot, 'Paro', on anxiety, agitation, depression, apathy, medication use, total sleep time, ...

The Role of Artificial Intelligence in Deciphering Diet-Disease Relationships: Case Studies.

Modernization of society from a rural, hunter-gatherer setting into an urban and industrial habitat, with the associated dietary changes, has led to a...

First case-series of robot-assisted pudendal nerve release: technique and outcomes.

OBJECTIVE: Pudendal Nerve Entrapment (PNE) may determine chronic pelvic pain associated with symptoms related to its innervation area. This study aime...

Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study.

BACKGROUND: Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatm...

Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson's disease.

Markerless motion capture methods are continuously in development to target limitations encountered in marker-, sensor-, or depth-based systems. Previ...

Deep learning application for the classification of Alzheimer's disease using F-flortaucipir (AV-1451) tau positron emission tomography.

The positron emission tomography (PET) with F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (A...

DGA3-Net: A parameter-efficient deep learning model for ASPECTS assessment for acute ischemic stroke using non-contrast computed tomography.

Detecting the early signs of stroke using non-contrast computerized tomography (NCCT) is essential for the diagnosis of acute ischemic stroke (AIS). H...

Sequence based local-global information fusion framework for vertebrae detection under pathological and FOV variation challenges.

Automated vertebrae detection (identification and localization) aims to identify vertebrae and locate their centroids in medical images, which is a cr...

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model.

BACKGROUND AND PURPOSE: Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for tr...

Automatic Identification of Ultrasound Images of the Tibial Nerve in Different Ankle Positions Using Deep Learning.

Peripheral nerve tension is known to be related to the pathophysiology of neuropathy; however, assessing this tension is difficult in a clinical setti...

Spider silk erectile nerve reconstruction in robot-assisted radical prostatectomy: a first-in-men feasibility analysis.

PURPOSE: To investigate the safety and feasibility of spider silk interposition for erectile nerve reconstruction in patients undergoing robotic radic...