Evidence map›Paper›PMID 32341536›Full record

ArticleEye (London, England)2021

Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography.

Tyson N Kim, Michael T Aaberg, Patrick Li, Jose R Davila, Malavika Bhaskaranand, Sandeep Bhat, Chaithanya Ramachandra, Kaushal Solanki, Frankie Myers, Clay Reber and 4 more

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In one paragraph

Article in Eye (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

14 authors.

Tyson N KimDepartment of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA. tyson.kim@ucsf.edu.
Michael T AabergDepartment of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA.
Patrick LiDepartment of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA.
Jose R DavilaDepartment of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA.
Malavika BhaskaranandEyenuk Inc, Woodland Hills, CA, USA.
Sandeep BhatEyenuk Inc, Woodland Hills, CA, USA.
Chaithanya RamachandraEyenuk Inc, Woodland Hills, CA, USA.
Kaushal SolankiEyenuk Inc, Woodland Hills, CA, USA.
Frankie MyersDepartment of Bioengineering, University of California Berkeley, Berkeley, CA, USA.
Clay ReberDepartment of Bioengineering, University of California Berkeley, Berkeley, CA, USA.
Rohan JalalizadehDepartment of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA.
Todd P MargolisDepartment of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO, USA.
Daniel FletcherDepartment of Bioengineering, University of California Berkeley, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-1890-5364
Yannis M PaulusDepartment of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA. ypaulus@med.umich.edu.

Funding

Real-time In Vivo Visualization of the Molecular Processes in Choroidal NeovascularizationK08EY027458 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PAULUS, YANNIS MANTAS · 2018 to 2021
$898k
NEI NIH HHS K08 EY027458
6 · The paper itself

Abstract

purposeThe aim of this study is to investigate the efficacy of a mobile platform that combines smartphone-based retinal imaging with automated grading for determining the presence of referral-warranted diabetic retinopathy (RWDR).

methodsA smartphone-based camera (RetinaScope) was used by non-ophthalmic personnel to image the retina of patients with diabetes. Images were analyzed with the Eyenuk EyeArt® system, which generated referral recommendations based on presence of diabetic retinopathy (DR) and/or markers for clinically significant macular oedema. Images were independently evaluated by two masked readers and categorized as refer/no refer. The accuracies of the graders and automated interpretation were determined by comparing results to gold standard clinical diagnoses.

resultsA total of 119 eyes from 69 patients were included. RWDR was present in 88 eyes (73.9%) and in 54 patients (78.3%). At the patient-level, automated interpretation had a sensitivity of 87.0% and specificity of 78.6%; grader 1 had a sensitivity of 96.3% and specificity of 42.9%; grader 2 had a sensitivity of 92.5% and specificity of 50.0%. At the eye-level, automated interpretation had a sensitivity of 77.8% and specificity of 71.5%; grader 1 had a sensitivity of 94.0% and specificity of 52.2%; grader 2 had a sensitivity of 89.5% and specificity of 66.9%. DISCUSSION: Retinal photography with RetinaScope combined with automated interpretation by EyeArt achieved a lower sensitivity but higher specificity than trained expert graders. Feasibility testing was performed using non-ophthalmic personnel in a retina clinic with high disease burden. Additional studies are needed to assess efficacy of screening diabetic patients from general population.

Indexed as

Diabetes MellitusDiabetic RetinopathyMacular EdemaHumansPhotographyRetinaSensitivity and SpecificitySmartphone

Identifiers

PMID32341536
PMCPMC7852658

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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.