Evidence map›Paper›PMID 26425849›Full record

ArticlePloS one2015

Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya.

Morten B Hansen, Michael D Abràmoff, James C Folk, Wanjiku Mathenge, Andrew Bastawrous, Tunde Peto

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In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 4 pooled it
–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

39 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  15. Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023
    Review
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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

6 authors.

Morten B HansenNIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophtalmology, London, United Kingdom; Research Unit of Ophthalmology, University of Southern Denmark, Odense, Denmark.
Michael D AbràmoffDepartment of Ophthalmology and Visual Sciences, University of Iowa Hospital and Clinics, Iowa City, IA, 52242, United States of America.
James C FolkDepartment of Ophthalmology and Visual Sciences, University of Iowa Hospital and Clinics, Iowa City, IA, 52242, United States of America.
Wanjiku MathengeRwanda International Institute of Ophthalmology, P.O. Box 312, Kigali, Rwanda.
Andrew BastawrousInternational Centre for Eye Health, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine (LSHTM), London, United Kingdom.
Tunde PetoNIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophtalmology, London, United Kingdom.

Funding

Three-dimensional structure-function relationships in glaucoma from SD-OCTR01EY018853 · NEI · UNIVERSITY OF IOWA · PI ABRAMOFF, MICHAEL DAVID, KWON, YOUNG H. · 2009 to 2016
$2.6M
Low-cost, portable, computer assisted imaging for diabe*R01EY017066 · NEI · UNIVERSITY OF IOWA · PI ABRAMOFF, MICHAEL DAVID · 2005 to 2008
$1.3M
Medical Research Council G1001934NEI NIH HHS R01 EY017066NEI NIH HHS R01EY017066NEI NIH HHS R01 EY018853NEI NIH HHS R01EY018853
6 · The paper itself

Abstract

objectiveDigital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya.

participantsRetinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]).

methodsFirst, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR.

main outcome measuresThe primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard.

resultsAltogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment.

conclusionsIn this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading.

Indexed as

Image Processing, Computer-AssistedMolecular ImagingRetinaAutomationDiabetic RetinopathyHumansKenyaSoftware

Identifiers

PMID26425849
PMCPMC4591009

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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.