Evidence map›Paper›PMID 36617971›Full record

SynthesisJournal of medical screening2023

Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.

Zhivko Zhelev, Jaime Peters, Morwenna Rogers, Michael Allen, Goda Kijauskaite, Farah Seedat, Elizabeth Wilkinson, Christopher Hyde

Open access · hybridAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of medical screening, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 4 pooled it
5.5field-weighted citation impact, top 4% 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

16 citing papers in PubMed, 4 syntheses or guidelines pooled it, 24 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
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

8 authors at 2 institutions in 1 country.

Zhivko ZhelevExeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK.ORCID 0000-0002-0106-2401
Jaime PetersExeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK.
Morwenna RogersNIHR ARC South West Peninsula (PenARC), University of Exeter Medical School, University of Exeter, Exeter, UK.
Michael AllenUniversity of Exeter Medical School, University of Exeter, Exeter, UK.
Goda KijauskaiteUK National Screening Committee, London, UK.
Farah SeedatUK National Screening Committee, London, UK.
Elizabeth WilkinsonNorthern Devon Healthcare NHS Trust, Devon, UK.
Christopher HydeExeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK.ORCID 0000-0002-7349-0616
University of Exeter · GBNorthern Devon Healthcare NHS Trust · GB

Funding

Department of Health
6 · The paper itself

Abstract

objectivesTo systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening.

methodsWe searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively.

resultsForty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders.

conclusionsAI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.

Indexed as

Diabetes MellitusDiabetic RetinopathyArtificial IntelligenceEarly Detection of CancerHumansMass ScreeningArtificial intelligencediabetic retinopathyfundus imagingscreeningsensitivity and specificitysystematic review

Identifiers

PMID36617971
PMCPMC10399100
OpenAlexW4313824522

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.