Evidence map›Paper›PMID 40089134›Full record

ReviewExperimental eye research2025

Publicly available imaging datasets for age-related macular degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.

Nayoon Gim, Alina Ferguson, Marian Blazes, Sanjay Soundarajan, Aydan Gasimova, Yu Jiang, Clara I Sánchez, Luca Zalunardo, Giulia Corradetti, Tobias Elze and 15 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Experimental eye research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. Article
  2. 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

25 authors.

Nayoon GimDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA; Department of Bioengineering, University of Washington, Seattle, WA, USA.
Alina FergusonDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA; University of Washington School of Medicine, Seattle, WA, USA.
Marian BlazesDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
Sanjay SoundarajanFAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, USA.
Aydan GasimovaFAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, USA.
Yu JiangDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
Clara I SánchezDepartment of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, the Netherlands; Quantitative Healthcare Analysis (QurAI) Group, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands.
Luca ZalunardoICare, Raleigh, NC, USA.
Giulia CorradettiDoheny Eye Institute, Pasadena, CA, USA; Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Tobias ElzeMass. Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
Naoto HondaNIDEK CO., LTD., Gamagori, Japan.
Nadia K WaheedNew England Eye Center, Boston, MA, USA.
Anne Marie CairnsOptos, Dunfermline, Scotland, United Kingdom.
M Valeria Canto-SolerCellSight Ocular Stem Cell and Regeneration Research Program, Department of Ophthalmology, Sue Anschutz-Rodgers Eye Center, University of Colorado, Aurora, CO, USA.
Amitha DomalpallyWisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
Mary DurbinTopcon Healthcare, Oakland, NJ, USA.
Daniela FerraraGenentech, South San Francisco, CA, USA.
Jewel HuDoheny Eye Institute, Pasadena, CA, USA.
Prashant NairProceedings of the National Academy of Sciences, Washington, DC, USA.
Aaron Y LeeDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
Srinivas R SaddaDoheny Eye Institute, Pasadena, CA, USA; Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Tiarnan D L KeenanDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
Bhavesh PatelFAIR Data Innovations Hub, California Medical Innovations Institute, San Diego, CA, USA.
Cecilia S LeeDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
Ryan Initiative For Macular Research (RIMR) Consortium

Funding

Aging eyes and aging brains in studying alzheimer's disease: Modern ophthalmic data collection in the adult changes in thought (ACT) studyR01AG060942 · NIA · WASHINGTON UNIVERSITY · PI Cecilia Sungmin Lee · 2019 to 2026
$39.4M
Bridge2AI:Salutogenesis Data Generation ProjectOT2OD032644 · OD · WASHINGTON UNIVERSITY · PI BAXTER, SALLY LIU, CHUTE, CHRISTOPHER G · 2022 to 2025
$32.7M
PILOT STUDY--CLINICAL NUTRITION RESEARCHP30DK035816 · NIDDK · UNIVERSITY OF WASHINGTON · PI GREGORY J MORTON · 1986 to 2026
$30.4M
VISION RESEARCHP30EY003790 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI Patricia Ann D'Amore · 1985 to 2026
$25.5M
NEI NIH HHS P30 EY003790NIA NIH HHS R01 AG060942NIDDK NIH HHS P30 DK035816NIH HHS OT2 OD032644
6 · The paper itself

Abstract

Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affecting more than 200 million people worldwide. With no cure currently available and a rapidly increasing prevalence, emerging approaches such as artificial intelligence (AI) and machine learning (ML) hold promise for advancing the study of AMD. The effective utilization of AI and ML in AMD research is highly dependent on access to high-quality and reusable clinical data. The Findable, Accessible, Interoperable, Reusable (FAIR) principles, published in 2016, provide a framework for sharing data that is easily useable by both humans and machines. However, it is unclear how these principles are implemented with regards to ophthalmic imaging datasets for AMD research. We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. The assessment revealed that none of the datasets were fully compliant with FAIR principles. Specifically, compliance rates were 5 % for Findable, 82 % for Accessible, 73 % for Interoperable, and 0 % for Reusable. The low compliance rates can be attributed to the relatively recent emergence of these principles and the lack of established standards for data and metadata formatting in the AMD research community. This article presents our findings and offers guidelines for adopting FAIR practices to enhance data sharing in AMD research.

Indexed as

Databases, FactualInformation DisseminationMacular DegenerationTomography, Optical CoherenceArtificial IntelligenceHumansMachine LearningAMDArtificial intelligenceData reuseData sharingFAIR dataMachine learningOCT dataset

Identifiers

PMID40089134
PMCPMC12058379

What OpenQuestion holds

Textmetadata
LicenceTDM
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.