Neurology

Non-inferiority of deep learning ischemic stroke segmentation on non-contrast CT within 16-hours compared to expert neuroradiologists.

We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflecte...

CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images.

BACKGROUND: Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment risks. However, manual segmentation of brain lesions relies on the experience of neurologists and is also a very tedious and time-consuming process. So, in this study, we proposed a...

State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review.

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in roboti...

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-inva...

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtypin...

Comparing pentafecta outcomes between nerve sparing and non nerve sparing robot-assisted radical prostatectomy in a propensity score-matched study.

Pentafecta (continence, potency, cancer control, free surgical margins, and no complications) is an important outcome of prostatectomy. Our objective ...

A bidirectional fabric-based soft robotic glove for hand function assistance in patients with chronic stroke.

BACKGROUND: Chronic stroke patients usually experience reduced hand functions, impeding their ability to perform activities of daily living (ADLs) ind...

Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury.

BACKGROUND: Walking impairments are a common consequence of neurological disorders and are assessed with clinical scores that suffer from several limi...

Overview of methods and available tools used in complex brain disorders.

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually ...

Robot-assisted versus open radical prostatectomy: a systematic review and meta-analysis of prospective studies.

The study aims to synthesize all available prospective comparative studies and reports the latest systematic analysis and updated evidence comparing r...

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Spiking neural networks (SNNs), as the third generation of neural networks, are based on biological models of human brain neurons. In this work, a sha...

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers.

The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker...

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes.

Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical managemen...

As artificial intelligence goes multimodal, medical applications multiply.

Machines don't have eyes, but you wouldn't know that if you followed the progression of deep learning models for accurate interpretation of medical im...

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalograp...

100 Complex posterior spinal fusion cases performed with robotic instrumentation.

Robotic navigation has been shown to increase precision, accuracy, and safety during spinal reconstructive procedures. There is a paucity of literatur...

Comparison of the carotid corrected flow time and tidal volume challenge for assessing fluid responsiveness in robot-assisted laparoscopic surgery.

We aimed to compare the ability of carotid corrected flow time assessed by ultrasound and the changes in dynamic preload indices induced by tidal volu...

Effect of exoskeleton robot-assisted training on gait function in chronic stroke survivors: a systematic review of randomised controlled trials.

OBJECTIVES: Numbers of research have reported the usage of robot-assisted gait training for walking restoration post-stroke. However, no consistent co...

Da Vinci Meets Globus Excelsius GPS: A Totally Robotic Minimally Invasive Anterior and Posterior Lumbar Fusion.

BACKGROUND: Minimally invasive approaches to the spine via anterior and posterior approaches have been increasing in popularity, culminating in the de...

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symp...