AIMC Topic: Neurologic Examination
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Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation.
Journal of Parkinson's disease
Feb 13, 2025
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...
Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning.
Annals of vascular surgery
Nov 22, 2024
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 ...
Humans
Machine Learning
Decision Support Techniques
Neurologic Examination
Middle Aged
Endarterectomy, Carotid
Risk Assessment
Risk Factors
Case-Control Studies
Aged
Intraoperative Neurophysiological Monitoring
Retrospective Studies
Treatment Outcome
Aged, 80 and over
Female
Predictive Value of Tests
Male
Reproducibility of Results
Wakefulness
Carotid Stenosis
The classification of flash visual evoked potential based on deep learning.
BMC medical informatics and decision making
Jan 19, 2023
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...
Olfactory Testing in Parkinson Disease and REM Behavior Disorder: A Machine Learning Approach.
Neurology
Feb 24, 2021
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).
Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements.
PloS one
Jan 29, 2021
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...
Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study.
Journal of medical Internet research
Sep 16, 2020
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...
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
Jul 25, 2020
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...
A Neuro-ontology for the neurological examination.
BMC medical informatics and decision making
Mar 4, 2020
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...
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
Sep 13, 2019
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 ...
Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.
JAMA network open
Jun 5, 2019
IMPORTANCE: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic.
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