Evidence map›Paper›PMID 38955958›Full record

ArticleDocumenta ophthalmologica. Advances in ophthalmology2024

Portable multi-focal visual evoked potential diagnostics for multiple sclerosis/optic neuritis patients.

S Mohammad Ali Banijamali, Craig Versek, Kristen Babinski, Sagar Kamarthi, Deborah Green-LaRoche, Srinivas Sridhar

Abstract read
In one paragraph

Article in Documenta ophthalmologica. Advances in ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

S Mohammad Ali BanijamaliDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.ORCID 0009-0004-1467-1726
Craig VersekNeuroFieldz Inc, Newton, MA, USA.
Kristen BabinskiDepartment of Neurology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA.
Sagar KamarthiDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA.
Deborah Green-LaRocheDepartment of Neurology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA.
Srinivas SridharNeuroFieldz Inc, Newton, MA, USA. s.sridhar@northeastern.edu.ORCID 0000-0003-1605-0734

Funding

Tufts Clinical and Translational Science Institute (Pilot Supplement)UL1TR002544 · NCATS · TUFTS UNIVERSITY BOSTON · PI SELKER, HARRY P. · 2018 to 2022
$50.2M
National Institute of Health UL1TR002544NCATS NIH HHS UL1 TR002544
6 · The paper itself

Abstract

purposeMultiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We've developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes.

methodsSubject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.

results20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.

conclusionThis pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.

Indexed as

Evoked Potentials, VisualMultiple SclerosisOptic NeuritisAdultElectroencephalographyFemaleHumansMaleMiddle AgedPhotic StimulationPilot ProjectsVisual CortexVisual FieldsFull-field visual evoked potential (ffVEP)Machine learningMulti-focal visual evoked potential (mfVEP)Multiple sclerosis (MS)Optic neuritisPortable diagnosticsSignal processingSupport vector machine (SVM)

Identifiers

PMID38955958
PMCPMC11236877

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.