Effectiveness of the iPhone GoCheck Kids smartphone vision screener in detecting amblyopia risk factors.


Journal

Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus
ISSN: 1528-3933
Titre abrégé: J AAPOS
Pays: United States
ID NLM: 9710011

Informations de publication

Date de publication:
02 2020
Historique:
received: 29 05 2019
revised: 30 09 2019
accepted: 01 10 2019
pubmed: 7 1 2020
medline: 16 6 2021
entrez: 7 1 2020
Statut: ppublish

Résumé

The recently released GoCheck Kids iPhone photoscreening app is designed to detect amblyopia risk factors (ARFs) in young children and includes remote review of images captured by smartphone. We aimed to evaluate the system's accuracy in detecting AAPOS guidelines ARFs. Patients 6 months through 6 years of age at Medical University of South Carolina were recruited for this prospective study. Presence of age-specific ARF was determined based on a complete ophthalmic examination and compared with the GoCheck Kids recommendations. A total of 244 children were included (average age, 42 months; 51% male). Sensitivity of the GoCheck Kids iPhone with remote review to detect ARFs was 90.5%; specificity, 68.1%; positive predictive value, 56.8%; negative predictive value, 94.0%. Two of the 7 false negative results had hyperopia of >4 D, 4 had astigmatism, and 1 had anisometropia. Remote review of all images improved sensitivity. The GoCheck Kids app had good sensitivity and adequate specificity in detecting AAPOS ARFs in our enriched cohort of young children.

Sections du résumé

BACKGROUND
The recently released GoCheck Kids iPhone photoscreening app is designed to detect amblyopia risk factors (ARFs) in young children and includes remote review of images captured by smartphone. We aimed to evaluate the system's accuracy in detecting AAPOS guidelines ARFs.
METHODS
Patients 6 months through 6 years of age at Medical University of South Carolina were recruited for this prospective study. Presence of age-specific ARF was determined based on a complete ophthalmic examination and compared with the GoCheck Kids recommendations.
RESULTS
A total of 244 children were included (average age, 42 months; 51% male). Sensitivity of the GoCheck Kids iPhone with remote review to detect ARFs was 90.5%; specificity, 68.1%; positive predictive value, 56.8%; negative predictive value, 94.0%. Two of the 7 false negative results had hyperopia of >4 D, 4 had astigmatism, and 1 had anisometropia. Remote review of all images improved sensitivity.
CONCLUSIONS
The GoCheck Kids app had good sensitivity and adequate specificity in detecting AAPOS ARFs in our enriched cohort of young children.

Identifiants

pubmed: 31904429
pii: S1091-8531(19)30582-8
doi: 10.1016/j.jaapos.2019.10.007
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

16.e1-16.e5

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2020 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.

Auteurs

Mollianna Walker (M)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Alba Duvall (A)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Mackenzie Daniels (M)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Mailynh Doan (M)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Luke E Edmondson (LE)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Edward W Cheeseman (EW)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

M Edward Wilson (ME)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Rupal H Trivedi (RH)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina.

Mae Millicent W Peterseim (MMW)

Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina. Electronic address: petersem@musc.edu.

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Classifications MeSH