Neurology

Predicting Diabetic Neuropathy Risk Level Using Artificial Neural Network and Clinical Parameters of Subjects With Diabetes.

BACKGROUND: A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy, which utilizes vibration perception threshold (VPT) and a set of clinical variables as potential predictors.

EEG-based deep learning model for the automatic detection of clinical depression.

Clinical depression is a neurological disorder that can be identified by analyzing the Electroencephalography (EEG) signals. However, the major drawback in using EEG to accurately identify depression is the complexity and variation that exist in the EEG of a depressed individual. There are several strategies for automated depression diagnosis, but they all have flaws, which make the diagnostic tas...

Unifying neural learning and symbolic reasoning for spinal medical report generation.

Automated medical report generation in spine radiology, i.e., given spinal medical images and directly create radiologist-level diagnosis reports to s...

CS-Net: Deep learning segmentation of curvilinear structures in medical imaging.

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in auto...

Predicting memory from study-related brain activity.

To isolate brain activity that may reflect effective cognitive processes during the study phase of a memory task, cognitive neuroscientists commonly c...

Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.

This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image informati...

A comparison of the effects and usability of two exoskeletal robots with and without robotic actuation for upper extremity rehabilitation among patients with stroke: a single-blinded randomised controlled pilot study.

BACKGROUND: Robotic rehabilitation of stroke survivors with upper extremity dysfunction may yield different outcomes depending on the robot type. Cons...

Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants.

The adjunctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot inte...

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for strok...

Effects of walking distance over robot-assisted training on walking ability in chronic stroke patients.

An understanding of the dose-response during training is important to identify the rehabilitation programs to obtain the improvement in chronic stroke...

Machine learning discriminates a movement disorder in a zebrafish model of Parkinson's disease.

Animal models of human disease provide an system that can reveal molecular mechanisms by which mutations cause pathology, and, moreover, have the pot...

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics.

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as ben...

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients...

Adoption of Single-Port Robotic Prostatectomy: Two Alternative Strategies.

To demonstrate two distinct methods for adopting the single-port (SP) robotic surgery system for robotic-assisted laparoscopic prostatectomy (RALP) b...

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG.

OBJECTIVE: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as ...

Towards Automated Emotion Classification of Atypically and Typically Developing Infants.

The World Health Organization estimates that 15 million infants are born preterm every year [1]. This is of concern because these infants have a signi...

Improvement in early continence after introduction of periurethral suspension stitch in robotic prostatectomy.

Early urinary incontinence remains a major source of morbidity for patients undergoing robotic prostatectomy. The purpose of the study was to determin...

Re: Paolo Afonso de Carvalho, Joāo A.B.A. Barbosa, Giuliano B. Guglielmetti, et al. Retrograde Release of the Neurovascular Bundle with Preservation of Dorsal Venous Complex During Robot-assisted Radical Prostatectomy: Optimizing Functional Outcomes. Eur Urol 2020;77:628-35: Incredible Results for Robot-assisted Nerve-sparing Radical Prostatectomy in Prostate Cancer Patients.

Virgin Coconut Oil-Induced Neuroprotection in Lipopolysaccharide-Challenged Rats is Mediated, in Part, Through Cholinergic, Anti-Oxidative and Anti-Inflammatory Pathways.

Neuroinflammation is associated with neuronal cell death and could lead to chronic neurodegeneration. This study investigated the neuroprotective pote...

Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

OBJECTIVE: The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with ...