Evidence map›Paper›PMID 36034311›Full record

ArticleFrontiers in neurology2022

aEYE: A deep learning system for video nystagmus detection.

Narayani Wagle, John Morkos, Jingyan Liu, Henry Reith, Joseph Greenstein, Kirby Gong, Indranuj Gangan, Daniil Pakhomov, Sanchit Hira, Oleg V Komogortsev and 5 more

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02483429 (Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage), which is not on this map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
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.

NCT02483429 phase2completednot on this map

Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage (AVERT)

TypeinterventionalSponsorJohns Hopkins UniversityRan2017 to 2023Enrolled130ConditionsVertigo, DizzinessArmsVRT Care
3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 34 citations in OpenAlex.

  1. Review
  2. Article
  3. Horizontal nystagmus identification with joint SAM segmentation and time series classification.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Article
  4. Acute vertigo: stroke or not?Current opinion in neurology · 2026
    Review
  5. Automated vertigo diagnosis using video-oculography and artificial intelligence for clinic-based triage: Preliminary results.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Article
  6. Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Neurological update: neuro-otology 2023.Journal of neurology · 2023
    Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 1 country.

Narayani WagleDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
John MorkosThe John Hopkins University School of Medicine, Baltimore, MD, United States.
Jingyan LiuDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
Henry ReithDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
Joseph GreensteinInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.
Kirby GongDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
Indranuj GanganDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
Daniil PakhomovDepartment of Computer Science, The Johns Hopkins University, Baltimore, MD, United States.
Sanchit HiraDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
Oleg V KomogortsevDepartment of Computer Science, Texas State University, San Marcos, TX, United States.
David E Newman-TokerInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.
Raimond WinslowDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
David S ZeeInstitute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.
Jorge Otero-MillanDepartment of Neurosciences, The John Hopkins University School of Medicine, Baltimore, MD, United States.
Kemar E GreenDepartment of Biomedical Engineering, The John Hopkins University, Baltimore, MD, United States.
Johns Hopkins University · USJohns Hopkins Medicine · USTexas State University · USUniversity of California, Berkeley · US

Funding

Research Supplement to Promote Diversity in Health-Related Research U01DC013778-01A1U01DC013778 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI NEWMAN-TOKER, DAVID · 2014 to 2018
$5.4M
NIDCD NIH HHS U01 DC013778
6 · The paper itself

Abstract

Background: Nystagmus identification and interpretation is challenging for non-experts who lack specific training in neuro-ophthalmology or neuro-otology. This challenge is magnified when the task is performed Methods: We developed, trained, and validated a deep-learning system (aEYE) to classify video recordings as normal or bearing at least two consecutive beats of nystagmus. The videos were retrospectively collected from a subset of the monocular (right eye) video-oculography (VOG) recording used in the Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage (AVERT) clinical trial (#NCT02483429). Our model was derived from a preliminary dataset representing about 10% of the total AVERT videos ( Results: An ensemble between the ResNet-soft voting and the VGG-hard voting models had the best performing metrics. The AUROC, sensitivity, specificity, and accuracy were 0.86, 88.4, 74.2, and 82.7%, respectively. Our validated folds had an average AUROC, sensitivity, specificity, and accuracy of 0.86, 80.3, 80.9, and 80.4%, respectively. Models created from the compressed videos decreased in accuracy as image sampling rate decreased from 60 Hz to 15 Hz. There was only minimal change in the accuracy of nystagmus detection when decreasing image resolution and keeping sampling rate constant. Conclusion: Deep learning is useful in detecting nystagmus in 60 Hz video recordings as well as videos with lower image resolutions and sampling rates, making it a potentially useful tool to aid future automated eye-movement enabled neurologic diagnosis.

Indexed as

artificial intelligencedeep learningdizzinesseye movementsmachine learningnystagmustelemedicinevertigo

Identifiers

PMID36034311
PMCPMC9403604
OpenAlexW4291016115

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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.