Evidence map›Paper›PMID 37641576›Full record

ArticleJournal of diabetes science and technology2025

Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.

John J Wroblewski, Ermilo Sanchez-Buenfil, Miguel Inciarte, Jay Berdia, Lewis Blake, Simon Wroblewski, Alexandria Patti, Gretchen Suter, George E Sanborn

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Article in Journal of diabetes science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Modern Approach to Diabetic Retinopathy Diagnostics.Diagnostics (Basel, Switzerland) · 2024
    Review
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

9 authors.

John J WroblewskiRetina Care International, Hagerstown, MD, USA.ORCID 0000-0002-0600-8666
Ermilo Sanchez-BuenfilRetimediQ, Mérida, Mexico.
Miguel InciarteRetimediQ, Mérida, Mexico.
Jay BerdiaCumberland Valley Retina Consultants, Hagerstown, MD, USA.
Lewis BlakeDepartment of Applied Mathematics and Statistics, Colorado School of Mines, Golden, CO, USA.
Simon WroblewskiCumberland Valley Retina Consultants, Hagerstown, MD, USA.
Alexandria PattiCumberland Valley Retina Consultants, Hagerstown, MD, USA.
Gretchen SuterCumberland Valley Retina Consultants, Hagerstown, MD, USA.
George E SanbornDepartment of Ophthalmology, Virginia Commonwealth University, Richmond, VA, USA.ORCID 0000-0002-0444-6988

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting.

methodsIn June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard.

resultsA total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%).

conclusionsMedios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.

Indexed as

Deep LearningDiabetic RetinopathyPhotographySmartphoneAdultAgedAlgorithmsArtificial IntelligenceFemaleFundus OculiHumansMaleMass ScreeningMiddle AgedRetrospective StudiesSensitivity and Specificityartificial intelligencediabetic retinopathyfundus-on-phone cameraMexicorural health carescreening

Identifiers

PMID37641576
PMCPMC11874329

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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.