Evidence map›Paper›PMID 34708760›Full record

ArticleIndian journal of ophthalmology2021

Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy.

Astha Jain, Radhika Krishnan, Ashwini Rogye, Sundaram Natarajan

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Indian journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
3.7field-weighted citation impact, top 6% 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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Astha JainDepartment of VItreoretina, Aditya Jyot Foundation for Twinkling Little Eyes, Mumbai, Maharashtra, India.
Radhika KrishnanDepartment of VItreoretina, Aditya Jyot Foundation for Twinkling Little Eyes, Mumbai, Maharashtra, India.
Ashwini RogyeDepartment of VItreoretina, Aditya Jyot Foundation for Twinkling Little Eyes, Mumbai, Maharashtra, India.
Sundaram NatarajanDepartment of VItreoretina, Aditya Jyot Eye Hospital, Major Parmeshwaran Road, Mumbai, Maharashtra, India.
Aditya Jyot Eye Hospital · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of the study was to analyse the reliability of an offline artificial intelligence (AI) algorithm for community screening of diabetic retinopathy.

methodsA total of 1378 patients with diabetes visiting public dispensaries under the administration of the Municipal Corporation of Greater Mumbai between August 2018 and September 2019 were enrolled for the study. Fundus images were captured by non-specialist operators using a smartphone-based camera covering the posterior pole, including the disc and macula, and the nasal and temporal fields. The offline AI algorithm on the smartphone marked the images as referable diabetic retinopathy (RDR) or non-RDR, which were then compared against the grading by two vitreoretinal surgeons to derive upon the sensitivity and specificity of the algorithm.

resultsOut of 1378 patients, gradable fundus images were obtained and analysed for 1294 patients. The sensitivity and specificity of diagnosing RDR were 100% (95% CI: 94.72-100.00%) and 89.55% (95% CI: 87.76-91.16%), respectively; the same values for any diabetic retinopathy (DR) were 89.13% (95% CI: 82.71-93.79%) and 94.43% (95% CI: 91.89-94.74%), respectively, with no false-negative results.

conclusionThe robustness of the offline AI algorithm was established in this study making it a reliable tool for community-based DR screening.

Indexed as

Diabetes MellitusDiabetic RetinopathyArtificial IntelligenceHumansMass ScreeningPhotographyReproducibility of ResultsSmartphoneArtificial intelligencecommunity screeningdiabetic retinopathyfundusretina

Identifiers

PMID34708760
PMCPMC8725118
OpenAlexW3210195560

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

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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.