Evidence map›Paper›PMID 33765984›Full record

ArticleBMC ophthalmology2021

A smartphone ocular alignment measurement app in school screening for strabismus.

Wenbo Cheng, Marissa H Lynn, Shrinivas Pundlik, Cheryl Almeida, Gang Luo, Kevin Houston

Open access · goldAbstract read
In one paragraph

Article in BMC ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 4 pooled it
2.7field-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.

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

15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Pooled it
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  11. Subjective Refraction Test Using a Smartphone for Vision Screening.Journal of visualized experiments : JoVE · 2024
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  13. Apple Vision Pro for Ophthalmology and Medicine.Annals of biomedical engineering · 2023
    Article
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  15. Evaluation of Endo 10 mobile application as diagnostic tool in endodontics.Journal of clinical and experimental dentistry · 2023
    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

6 authors at 4 institutions in 2 countries.

Wenbo ChengDepartment of Ophthalmology, The First Affiliated Hospital, Xinjiang Medical University, 137 Liyvshan Road. Urumqi, Xinjiang, 830000, China. chengwenbo123@126.com.
Marissa H LynnSchepens Eye Research Institute, Massachusetts Eye & Ear, Harvard Medical School, Boston, MA, USA.
Shrinivas PundlikSchepens Eye Research Institute, Massachusetts Eye & Ear, Harvard Medical School, Boston, MA, USA.
Cheryl AlmeidaWilliam G. Vinal School, Norwell, MA, USA.
Gang LuoSchepens Eye Research Institute, Massachusetts Eye & Ear, Harvard Medical School, Boston, MA, USA.
Kevin HoustonSchepens Eye Research Institute, Massachusetts Eye & Ear, Harvard Medical School, Boston, MA, USA.
Smith-Kettlewell Eye Research Institute · USHarvard University · USMassachusetts Eye and Ear Infirmary · USXinjiang Medical University · CN

Funding

National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541
6 · The paper itself

Abstract

backgroundStrabismus is the leading risk factor for amblyopia, which should be early detected for minimized visual impairment. However, traditional school screening for strabismus can be challenged due to several factors, most notably training, mobility and cost. The purpose of our study is to evaluate the feasibility of using a smartphone application in school vision screening for detection of strabismus.

methodsThe beta smartphone application, EyeTurn, can measure ocular misalignment by computerized Hirschberg test. The application was used by a school nurse in a routine vision screening for 133 elementary school children. All app measurements were reviewed by an ophthalmologist to assess the rate of successful measurement and were flagged for in-person verification with prism alternating cover test (PACT) using a 2.4Δ threshold (root mean squared error of the app). A receiver operating characteristic (ROC) curve was used to determine the best sensitivity and specificity for an 8Δ threshold (recommended by AAPOS) with the PACT measurement as ground truth.

resultsThe nurse obtained at least one successful app measurement for 93% of children (125/133). 40 were flagged for PACT, of which 6 were confirmed to have strabismus, including 4 exotropia (10△, 10△, 14△ and 18△), 1 constant esotropia (25△) and 1 accommodative esotropia (14△). Based on the ROC curve, the optimum threshold for the app to detect strabismus was determined to be 3.0△, with the best sensitivity (83.0%), specificity (76.5%). With this threshold the app would have missed one child with accommodative esotriopia, whereas conventional screening missed 3 cases of intermittent extropia.

conclusionsResults support feasibility of use of the app by personnel without professional training in routine school screenings to improve detection of strabismus.

Indexed as

AmblyopiaMobile ApplicationsStrabismusChildHumansSchoolsSmartphoneSchool screeningSmartphone applicationStrabismus

Identifiers

PMID33765984
PMCPMC7992982
OpenAlexW3113044496

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.