Evidence map›Paper›PMID 30280006›Full record

ArticleTranslational vision science & technology2018

A Smartphone-Based Tool for Rapid, Portable, and Automated Wide-Field Retinal Imaging.

Tyson N Kim, Frank Myers, Clay Reber, P J Loury, Panagiota Loumou, Doug Webster, Chris Echanique, Patrick Li, Jose R Davila, Robi N Maamari and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
9.9field-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.

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

49 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Smartphone-based retinal camera modified for ROP screening.International journal of retina and vitreous · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Modern Approach to Diabetic Retinopathy Diagnostics.Diagnostics (Basel, Switzerland) · 2024
    Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Deep Ensemble Learning for Retinal Image Classification.Translational vision science & technology · 2022
    Article
  18. Article
  19. Article
  20. Article
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

16 authors at 4 institutions in 1 country.

Tyson N KimDepartment of Ophthalmology and Visual Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA.
Frank MyersDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
Clay ReberDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
P J LouryDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
Panagiota LoumouDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
Doug WebsterDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
Chris EchaniqueDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
Patrick LiDepartment of Ophthalmology and Visual Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA.
Jose R DavilaDepartment of Ophthalmology and Visual Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA.
Robi N MaamariDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
Neil A SwitzDepartment of Physics and Astronomy, San José State University, San Jose, CA, USA.
Jeremy KeenanDepartment of Ophthalmology, University of California, San Francisco, CA, USA.
Maria A WoodwardDepartment of Ophthalmology and Visual Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA.
Yannis M PaulusDepartment of Ophthalmology and Visual Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA.
Todd MargolisDepartment of Ophthalmology and Visual Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Daniel A FletcherDepartment of Bioengineering and Biophysics Program, University of California, Berkeley, Berkeley, CA, USA.
University of California, Berkeley · USUniversity of Michigan–Ann Arbor · USSan Jose State University · USUniversity of California, San Francisco · US

Funding

Telemedicine For Anterior Eye DiseasesK23EY023596 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WOODWARD, MARIA ANNEKE · 2013 to 2017
$1.1M
NEI NIH HHS K23 EY023596
6 · The paper itself

Abstract

purposeHigh-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging.

methodsThe system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity.

resultsThe system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%.

conclusionsAutomation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening. TRANSLATIONAL RELEVANCE: Enhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening.

Indexed as

diabetic retinopathyophthalmoscopyretinal imagingsmartphone

Identifiers

PMID30280006
PMCPMC6166894
OpenAlexW2853074466

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.