Evidence map›Paper›PMID 32071178›Full record

ArticleBMJ open2020

Development of a system based on artificial intelligence to identify visual problems in children: study protocol of the TrackAI project.

Victoria Pueyo, Teresa Pérez-Roche, Esther Prieto, Olimpia Castillo, Inmaculada Gonzalez, Adrian Alejandre, Xian Pan, Alvaro Fanlo-Zarazaga, Juan Pinilla, Jose Ignacio Echevarria and 6 more

Open access · goldAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 17% 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

9 citing papers in PubMed, 18 citations in OpenAlex.

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  9. The Red Reflex Test - Shadow conceals, light reveals.Indian journal of ophthalmology · 2021
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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

16 authors at 3 institutions in 1 country.

Victoria PueyoInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain vpueyo@unizar.es.ORCID 0000-0002-1777-0349
Teresa Pérez-RocheInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Esther PrietoInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Olimpia CastilloInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Inmaculada GonzalezInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Adrian AlejandreInstituto de Investigacion en Ingeniería de Aragon, Universidad de Zaragoza, Zaragoza, Aragón, Spain.
Xian PanInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Alvaro Fanlo-ZarazagaInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Juan PinillaInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Jose Ignacio EchevarriaInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Diego GutierrezInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Irene AltemirInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
María Romero-SanzInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Marta CipresInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Marta OrtinInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Belen MasiaInstituto de Investigacion Sanitaria de Aragon, Zaragoza, Spain.
Hospital Universitario Miguel Servet · ESUniversidad de Zaragoza · ESInstituto de Investigación Sanitaria Aragón · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAround 70% to 80% of the 19 million visually disabled children in the world are due to a preventable or curable disease, if detected early enough. Vision screening in childhood is an evidence-based and cost-effective way to detect visual disorders. However, current screening programmes face several limitations: training required to perform them efficiently, lack of accurate screening tools and poor collaboration from young children.Some of these limitations can be overcome by new digital tools. Implementing a system based on artificial intelligence systems avoid the challenge of interpreting visual outcomes.The objective of the TrackAI Project is to develop a system to identify children with visual disorders. The system will have two main components: a novel visual test implemented in a digital device, DIVE (Device for an Integral Visual Examination); and artificial intelligence algorithms that will run on a smartphone to analyse automatically the visual data gathered by DIVE. METHODS AND ANALYSIS: This is a multicentre study, with at least five centres located in five geographically diverse study sites participating in the recruitment, covering Europe, USA and Asia.The study will include children aged between 6 months and 14 years, both with normal or abnormal visual development.The project will be divided in two consecutive phases: design and training of an artificial intelligence (AI) algorithm to identify visual problems, and system development and validation. The study protocol will consist of a comprehensive ophthalmological examination, performed by an experienced paediatric ophthalmologist, and an exam of the visual function using a DIVE.For the first part of the study, diagnostic labels will be given to each DIVE exam to train the neural network. For the validation, diagnosis provided by ophthalmologists will be compared with AI system outcomes. ETHICS AND DISSEMINATION: The study will be conducted in accordance with the principles of Good Clinical Practice. This protocol was approved by the Clinical Research Ethics Committee of Aragón, CEICA, on January 2019 (Code PI18/346).Results will be published in peer-reviewed journals and disseminated in scientific meetings. TRIAL REGISTRATION NUMBER: ISRCTN17316993.

Indexed as

Artificial IntelligenceAdolescentAmblyopiaAsiaChildChild, PreschoolClinical Trials as TopicCost-Benefit AnalysisEuropeHumansInfantMulticenter Studies as TopicSmartphoneUnited StatesVision DisordersVision Screeningartificial intelligencechildhoodvision screeningvisual impairment

Identifiers

PMID32071178
PMCPMC7044912
OpenAlexW3007187104

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.