Evidence map›Paper›PMID 30646178›Full record

ArticleJAMA network open2018

Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care.

Yogesan Kanagasingam, Di Xiao, Janardhan Vignarajan, Amita Preetham, Mei-Ling Tay-Kearney, Ateev Mehrotra

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAMA network open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05689437 (MIRA Clinical Learning Environment), which is not on this map. Cited by 98 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
98citing papers in PubMed, 9 pooled it
14.9field-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.

NCT05689437 unknown statusnot on this mapstarted 2022, after this paper: background citation

MIRA Clinical Learning Environment (MIRACLE): Lung

TypeobservationalSponsorUniversity Health Network, TorontoRan2022 to 2023Enrolled1,000ConditionsLung CancerArmsApplication of ILD prediction machine learning model to planning imaging, Routine, automatic presentation of ILD risk level for evaluation by the clinician., Application of SGR machine learning model to diagnostic and planning imaging, Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician., Application of CBCT machine learning model to on-treatment imaging
3 · Its place in the literature

Who cites it

98 citing papers in PubMed, 9 syntheses or guidelines pooled it, 187 citations in OpenAlex.

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  20. AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025
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38 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 2 countries.

Yogesan KanagasingamAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Perth, Western Australia, Australia.
Di XiaoAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Perth, Western Australia, Australia.
Janardhan VignarajanAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Perth, Western Australia, Australia.
Amita PreethamGP Super Clinic at Midland Railway Workshops, Midland, Western Australia, Australia.
Mei-Ling Tay-KearneyDepartment of Ophthalmology, Royal Perth Hospital, Perth, Western Australia, Australia.
Ateev MehrotraDepartment of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
Commonwealth Scientific and Industrial Research Organisation · AUAustralian e-Health Research Centre · AUHarvard University · USQueensland Rail · AURoyal Perth Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: There has been wide interest in using artificial intelligence (AI)-based grading of retinal images to identify diabetic retinopathy, but such a system has never been deployed and evaluated in clinical practice. Objective: To describe the performance of an AI system for diabetic retinopathy deployed in a primary care practice. Design, Setting, and Participants: Diagnostic study of patients with diabetes seen at a primary care practice with 4 physicians in Western Australia between December 1, 2016, and May 31, 2017. A total of 193 patients consented for the study and had retinal photographs taken of their eyes. Three hundred eighty-six images were evaluated by both the AI-based system and an ophthalmologist. Main Outcomes and Measures: Sensitivity and specificity of the AI system compared with the gold standard of ophthalmologist evaluation. Results: Of the 193 patients (93 [48%] female; mean [SD] age, 55 [17] years [range, 18-87 years]), the AI system judged 17 as having diabetic retinopathy of sufficient severity to require referral. The system correctly identified 2 patients with true disease and misclassified 15 as having disease (false-positives). The resulting specificity was 92% (95% CI, 87%-96%), and the positive predictive value was 12% (95% CI, 8%-18%). Many false-positives were driven by inadequate image quality (eg, dirty lens) and sheen reflections. Conclusions and Relevance: The results demonstrate both the potential and the challenges of using AI systems to identify diabetic retinopathy in clinical practice. Key challenges include the low incidence rate of disease and the related high false-positive rate as well as poor image quality. Further evaluations of AI systems in primary care are needed.

Indexed as

AdolescentAdultAgedAged, 80 and overArtificial IntelligenceDiabetic RetinopathyFemaleHumansMaleMiddle AgedPrimary Health CareResearch DesignSensitivity and SpecificityWestern Australia

Identifiers

PMID30646178
PMCPMC6324474
OpenAlexW2892773404

What OpenQuestion holds

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Registered trials

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.