Evidence map›Paper›PMID 36531575›Full record

ArticleOphthalmology science2022

Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting: The RAIDERS Randomized Trial.

Wanjiku Mathenge, Noelle Whitestone, John Nkurikiye, Jennifer L Patnaik, Prabhath Piyasena, Parfait Uwaliraye, Gabriella Lanouette, Malik Y Kahook, David H Cherwek, Nathan Congdon and 1 more

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

Article in Ophthalmology science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 5 pooled it
8.8field-weighted citation impact, top 1% 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

54 citing papers in PubMed, 5 syntheses or guidelines pooled it, 64 citations in OpenAlex.

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  17. Artificial intelligence in ophthalmology clinical trials: a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
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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

11 authors at 4 institutions in 4 countries.

Wanjiku MathengeRwanda International Institute of Ophthalmology, Kigali, Rwanda.
Noelle WhitestoneOrbis International, New York, New York.
John NkurikiyeRwanda International Institute of Ophthalmology, Kigali, Rwanda.
Jennifer L PatnaikOrbis International, New York, New York.
Prabhath PiyasenaCentre for Public Health, Queen's University Belfast, Belfast, United Kingdom.
Parfait UwalirayeMinistry of Health Rwanda, Kigali, Rwanda.
Gabriella LanouetteOrbis International, New York, New York.
Malik Y KahookDepartment of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado.
David H CherwekOrbis International, New York, New York.
Nathan CongdonOrbis International, New York, New York.
Nicolas JaccardOrbis International, New York, New York.
Queen's University Belfast · GBUniversity of Colorado Denver · USMinistry of Health · RWRwanda Military Hospital · RW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda. Design: The Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial. Participants: Patients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation. Methods: The RAIDERS study screened for DR using retinal imaging with AI interpretation implemented at 4 facilities from March 2021 through July 2021. Eligible participants were assigned randomly (1:1) to immediate feedback of AI grading (intervention) or communication of referral advice after human grading was completed 3 to 5 days after the initial screening (control). Main Outcome Measures: Difference between study groups in the rate of presentation for referral services within 30 days of being informed of the need for a referral visit. Results: Of the 823 clinic patients who met inclusion criteria, 275 participants (33.4%) showed positive findings for referable DR based on AI screening and were randomized for inclusion in the trial. Study participants (mean age, 50.7 years; 58.2% women) were randomized to the intervention (n = 136 [49.5%]) or control (n = 139 [50.5%]) groups. No significant intergroup differences were found at baseline, and main outcome data were available for analyses for 100% of participants. Referral adherence was statistically significantly higher in the intervention group (70/136 [51.5%]) versus the control group (55/139 [39.6%]; Conclusions: Immediate feedback on referral status based on AI-supported screening was associated with statistically significantly higher referral adherence compared with delayed communications of results from human graders. These results provide evidence for an important benefit of AI screening in promoting adherence to prescribed treatment for diabetic eye care in sub-Saharan Africa.

Indexed as

AI, artificial intelligenceArtificial intelligenceCI, confidence intervalDiabetic retinopathy screeningDR, diabetic retinopathyOR, odds ratioRAIDERS, Rwanda Artificial Intelligence for Diabetic Retinopathy ScreeningRandomized trialReferral uptakeRwandaSMS, short message service

Identifiers

PMID36531575
PMCPMC9754978
OpenAlexW4225161532

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.