Evidence map›Paper›PMID 40360070›Full record

ReviewProgress in retinal and eye research2025

The march to harmonized imaging standards for retinal imaging.

Nayoon Gim, Alina N Ferguson, Marian Blazes, Cecilia S Lee, Aaron Y Lee

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Progress in retinal and eye research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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

5 authors.

Nayoon GimDepartment 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; Department of Bioengineering, University of Washington, Seattle, WA, USA.
Alina N 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.
Cecilia S LeeDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA.
Aaron Y LeeDepartment of Ophthalmology, University of Washington, Seattle, WA, USA; Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA. Electronic address: leeay@uw.edu.

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 AYYAGARI, RADHA, BAHMANI, AMIR · 2022 to 2025
$32.7M
NIA NIH HHS R01 AG060942NIH HHS OT2 OD032644
6 · The paper itself

Abstract

The adoption of standardized imaging protocols in retinal imaging is critical to overcoming challenges posed by fragmented data formats across devices and manufacturers. The lack of standardization hinders clinical interoperability, collaborative research, and the development of artificial intelligence (AI) models that depend on large, high-quality datasets. The Digital Imaging and Communication in Medicine (DICOM) standard offers a robust solution for ensuring interoperability in medical imaging. Although DICOM is widely utilized in radiology and cardiology, its adoption in ophthalmology remains limited. Retinal imaging modalities such as optical coherence tomography (OCT), fundus photography, and OCT angiography (OCTA) have revolutionized retinal disease management but are constrained by proprietary and non-standardized formats. This review underscores the necessity for harmonized imaging standards in ophthalmology, detailing DICOM standards for retinal imaging including ophthalmic photography (OP), OCT, and OCTA, and their requisite metadata information. Additionally, the potential of DICOM standardization for advancing AI applications in ophthalmology is explored. A notable example is the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) dataset, the first publicly available standards-compliant DICOM retinal imaging dataset. This dataset encompasses diverse retinal imaging modalities, including color fundus photography, infrared, autofluorescence, OCT, and OCTA. By leveraging multimodal retinal imaging, AI-READI provides a transformative resource for studying diabetes and its complications, setting a blueprint for future datasets aimed at harmonizing imaging formats and enabling AI-driven breakthroughs in ophthalmology. Our manuscript also addresses challenges in retinal imaging for diabetic patients, retinal imaging-based AI applications for studying diabetes, and potential advancements in retinal imaging standardization.

Indexed as

Diagnostic Techniques, OphthalmologicalOphthalmologyRetinaRetinal DiseasesTomography, Optical CoherenceArtificial IntelligenceFluorescein AngiographyHumansArterial intelligenceArtificial intelligence ready and equitable atlas for diabetes insightsData standardizationDiabetic retinopathyDICOMImagingInteroperability

Identifiers

PMID40360070
PMCPMC12240699

What Socratic 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 Socratic graph.