Evidence map›Paper›PMID 40491217›Full record

ArticleClinical & experimental ophthalmology

Development and Validation of a Deep Learning System for the Provision of a District-Wide Diabetes Retinal Screening Service.

Jason R Daley, Xingdi Wang, Natalie Ngo, Chee L Khoo, Peter Heydon, Gerald Liew, Vallimayil Vallayutham, Tobias Kongbrailatpam, Uchechukwu L Osuagwu, Marko Andric and 3 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical & experimental ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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

13 authors.

Jason R DaleyLiverpool Hospital, Sydney, Australia.ORCID 0000-0003-3355-8193
Xingdi WangLiverpool Hospital, Sydney, Australia.
Natalie NgoLiverpool Hospital, Sydney, Australia.
Chee L KhooThe University of New South Wales, Sydney, Australia.
Peter HeydonLiverpool Hospital, Sydney, Australia.ORCID 0000-0001-7029-4188
Gerald LiewThe University of Sydney, Sydney, Australia.
Vallimayil VallayuthamThe University of Sydney, Sydney, Australia.
Tobias KongbrailatpamWestern Sydney University, Sydney, Australia.
Uchechukwu L OsuagwuWestern Sydney University, Sydney, Australia.
Marko AndricCampbelltown Hospital, Sydney, Australia.
Wei XuanThe University of New South Wales, Sydney, Australia.
David SimmonsCampbelltown Hospital, Sydney, Australia.
Shweta KaushikThe University of Sydney, Sydney, Australia.

Funding

Ophthalmic Research Institute of Australia
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) enhanced retinal screening could reduce the impact of diabetic retinopathy (DR), the leading cause of preventable blindness in Australia. This study assessed the performance and validity of a dual-modality, deep learning system for detection of vision-threatening diabetic retinopathy (vtDR) in a multi-ethnic community.

methodsCross-sectional (algorithm-validation) study with the deep learning system assessing fundus photographs for gradability and severity of DR, and optical coherence tomography (OCT) scans for diabetic macular oedema (DMO). Internal validation of each algorithm was performed using a computer-randomised 80:20 split. External validation was by comparison to standard grading provided by two ophthalmologists in 748 prospectively recruited persons with diabetes (age ≥ 10) from hospital diabetes clinics and a general practice. Main outcome measures included sensitivity, specificity and the area under the receiver operating characteristic curve (AUC).

resultsInternal validation revealed robust test characteristics. When compared to ophthalmologists, the system achieved an AUC of 0.92 (95% CI 0.90-0.94) for fundus photograph gradeability, 0.91 (95% CI 0.85-0.94) for the diagnosis of severe non-proliferative DR/proliferative DR and 0.90 (95% CI 0.87-0.96) for DMO detection from OCT scans. It demonstrated real-world applicability with an AUC of 0.94 (95% CI 0.91-0.97), sensitivity of 92.7% and specificity of 95.5% for detection of vtDR. Ungradable images occurred in 55 participants (7.4%).

conclusionsThe dual-modality, deep learning system can diagnose vtDR from fundus photographs and OCT scans with high levels of accuracy and specificity. This could support a novel model of care for DR screening in our community.

Indexed as

Deep LearningDiabetic RetinopathyMass ScreeningAdultAgedAlgorithmsArea Under CurveAustraliaCross-Sectional StudiesFemaleHumansMacular EdemaMaleMiddle AgedPhotographyProspective Studiesartificial intelligencedeep learningdiabetic retinopathydual‐modalityscreening

Identifiers

PMID40491217
PMCPMC12516661

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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.