Evidence map›Paper›PMID 40105843›Full record

ArticleJAMA network open2025

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

Arthur Brant, Preeti Singh, Xiang Yin, Lu Yang, Jay Nayar, Divleen Jeji, Yossi Matias, Greg S Corrado, Dale R Webster, Sunny Virmani and 8 more

Erratum issuedAbstract readEvaluation Study
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Article
  2. Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Review
  8. Errors in Results.JAMA network open · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Arthur BrantOregon Vision and Pharmacy LLC, Keizer.
Preeti SinghGoogle LLC, Mountain View, California.
Xiang YinGoogle LLC, Mountain View, California.
Lu YangGoogle LLC, Mountain View, California.
Jay NayarGoogle LLC, Mountain View, California.
Divleen JejiGoogle LLC, Mountain View, California.
Yossi MatiasGoogle LLC, Mountain View, California.
Greg S CorradoGoogle LLC, Mountain View, California.
Dale R WebsterGoogle LLC, Mountain View, California.
Sunny VirmaniGoogle LLC, Mountain View, California.
Anchintha MeenuAravind Eye Hospital, Madurai, India.
Naresh Babu KannanAravind Eye Hospital, Madurai, India.
Jonathan KrauseGoogle LLC, Mountain View, California.
Florence ThngGoogle LLC, Mountain View, California.
Lily PengVerily Life Sciences LLC, South San Francisco, California.
Yun LiuGoogle LLC, Mountain View, California.
Kasumi WidnerGoogle LLC, Mountain View, California.
Kim RamasamyAravind Eye Hospital, Madurai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of these algorithms. Objective: To evaluate the clinical performance of an automated retinal disease assessment (ARDA) algorithm in the postdeployment setting at Aravind Eye Hospital in India. Design, Setting, and Participants: This cross-sectional analysis involved an approximate 1% sample of fundus photographs from patients screened using ARDA. Images were graded via adjudication by US ophthalmologists for DR and DME, and ARDA's output was compared against the adjudicated grades at 45 sites in Southern India. Patients were randomly selected between January 1, 2019, and July 31, 2023. Main Outcomes and Measures: Primary analyses were the sensitivity and specificity of ARDA for severe nonproliferative DR (NPDR) or proliferative DR (PDR). Secondary analyses focused on sensitivity and specificity for sight-threatening DR (STDR) (DME or severe NPDR or PDR). Results: Among the 4537 patients with 4537 images with adjudicated grades, mean (SD) age was 55.2 (11.9) years and 2272 (50.1%) were male. Among the 3941 patients with gradable photographs, 683 (17.3%) had any DR, 146 (3.7%) had severe NPDR or PDR, 109 (2.8%) had PDR, and 398 (10.1%) had STDR. ARDA's sensitivity and specificity for severe NPDR or PDR were 97.0% (95% CI, 92.6%-99.2%) and 96.4% (95% CI, 95.7%-97.0%), respectively. Positive predictive value (PPV) was 50.7% and negative predictive value (NPV) was 99.9%. The clinically important miss rate for severe NPDR or PDR was 0% (eg, some patients with severe NPDR or PDR were interpreted as having moderate DR and referred to clinic). ARDA's sensitivity for STDR was 95.9% (95% CI, 93.0%-97.4%) and specificity was 94.9% (95% CI, 94.1%-95.7%); PPV and NPV were 67.9% and 99.5%, respectively. Conclusions and Relevance: In this cross-sectional study investigating the clinical performance of ARDA, sensitivity and specificity for severe NPDR and PDR exceeded 96% and caught 100% of patients with severe  NPDR and PDR for ophthalmology referral. This preliminary large-scale postmarketing report of the performance of ARDA after screening 600 000 patients in India underscores the importance of monitoring and publication an algorithm's clinical performance, consistent with recommendations by regulatory bodies.

Indexed as

Deep LearningDiabetic RetinopathyDiagnostic Techniques, OphthalmologicalAdultAgedCross-Sectional StudiesFemaleHumansMacular EdemaMaleSensitivity and Specificity

Identifiers

PMID40105843
PMCPMC11923701

What OpenQuestion holds

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LicenceCC BY
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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.