Evidence map›Paper›PMID 40688101›Full record

ArticleDigital biomarkers

A Pilot Study of Smartphone Eye Tracking for Detection of Positional Nystagmus.

Vidith Phillips, Pouya B Bastani, Hector Rieiro, David E Hale, Jorge Otero-Millan, David S Zee, David E Newman-Toker, Ali Saber Tehrani

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In one paragraph

Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. 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

8 authors.

Vidith PhillipsDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Pouya B BastaniDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Hector RieiroDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
David E HaleDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jorge Otero-MillanHerbert Wertheim School of Optometry and Vision Science, University of California Berkeley, Berkeley, CA, USA.
David S ZeeDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
David E Newman-TokerDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ali Saber TehraniDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Detecting positional nystagmus is essential for diagnosing benign paroxysmal positional vertigo (BPPV). Therefore, developing methods to streamline this diagnosis can improve timely patient management and help prevent unnecessary emergency department visits. We aimed to evaluate the accuracy of a smartphone eye-tracking application in quantifying eye movements during positional testing to detect positional nystagmus. Methods: We recruited patients with positional dizziness suspected of having BPPV from the vestibular rehabilitation clinic and the consult service for dizzy patients (Tele-Dizzy) at Johns Hopkins Hospital. Using an in-house smartphone app (EyePhone), we recorded eye movements during the Dix-Hallpike and supine roll tests. Two expert clinicians reviewed the videos, and a third one adjudicated the disagreements. Eye position data obtained from the EyePhone app were analyzed with an embedded algorithm to identify positional nystagmus. Using the adjudicated expert review as the reference standard, we evaluated EyePhone's accuracy in detecting positional nystagmus by calculating the sensitivity, specificity, and predictive values. Results: We recruited ten participants, 60% women, with an average age of 61.8 years. We reviewed 23 positional eye movement videos of participants while undergoing positional testing. The final adjudicated expert review identified positional nystagmus in 3 (13%) videos. The phone application traces indicated nystagmus in all 3 of these videos (sensitivity = 100% [95% CI = 44-100%]) and correctly ruled it out in 20 traces (specificity = 100% [95% CI = 84-100%]). The app demonstrated a positive predictive value of 100% (95% CI = 43-100%) and a negative predictive value of 100% (95% CI = 84-100%). Conclusions: This small pilot study shows proof-of-concept that a smartphone eye-tracking app without special phone attachments can detect positional nystagmus.

Indexed as

Benign paroxysmal positional vertigoEyePhonePositional nystagmusSmartphone eye trackingVestibular diagnostics

Identifiers

PMID40688101
PMCPMC12274059

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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.