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Neurologic Examination

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Validation of a robot serious game assessment protocol for upper limb motor impairment in children with cerebral palsy.

NeuroRehabilitation
BACKGROUND: The ROBiGAME project aims to implement serious games on robots to rehabilitate upper limb (UL) motor function in children with cerebral palsy (CP). Serious game characteristics (target position, level of assistance/resistance, level of fo...

Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Traumatic spinal cord injury can have a dramatic effect on a patient's life. The degree of neurologic recovery greatly influences a patient's treatment and expected quality of life. This has resulted in the development of machine ...

A Neuro-ontology for the neurological examination.

BMC medical informatics and decision making
BACKGROUND: The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as...

Robotic tests for position sense and movement discrimination in the upper limb reveal that they each are highly reproducible but not correlated in healthy individuals.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robotic technologies for neurological assessment provide sensitive, objective measures of behavioural impairments associated with injuries or disease such as stroke. Previous robotic tasks to assess proprioception typically involve single...

Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study.

Journal of medical Internet research
BACKGROUND: Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess th...

Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements.

PloS one
OBJECTIVE: One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of hu...

Olfactory Testing in Parkinson Disease and REM Behavior Disorder: A Machine Learning Approach.

Neurology
OBJECTIVE: We sought to identify an abbreviated test of impaired olfaction amenable for use in busy clinical environments in prodromal (isolated REM sleep behavior disorder [iRBD]) and manifest Parkinson disease (PD).

The classification of flash visual evoked potential based on deep learning.

BMC medical informatics and decision making
BACKGROUND: Visual electrophysiology is an objective visual function examination widely used in clinical work and medical identification that can objectively evaluate visual function and locate lesions according to waveform changes. However, in visua...

Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.

Annals of vascular surgery
BACKGROUND: Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered ...

Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.

Journal of Parkinson's disease
BackgroundClinical assessments of motor symptoms rely on observations and subjective judgments against standardized scales, leading to variability due to confounders. Improving inter-rater agreement is essential for effective disease management.Objec...