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The Retina01:32

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The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
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The march to harmonized imaging standards for retinal imaging.

Nayoon Gim1, Alina N Ferguson2, Marian Blazes3

  • 1Department 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.

Progress in Retinal and Eye Research
|May 13, 2025
PubMed
Summary

Standardizing retinal imaging with Digital Imaging and Communication in Medicine (DICOM) is crucial for AI development and clinical research. This enables interoperable, high-quality datasets for advancing ophthalmology.

Keywords:
Arterial intelligenceArtificial intelligence ready and equitable atlas for diabetes insightsDICOMData standardizationDiabetic retinopathyImagingInteroperability

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Fragmented data formats in retinal imaging hinder interoperability, research, and AI development.
  • Limited adoption of Digital Imaging and Communication in Medicine (DICOM) in ophthalmology contrasts with its use in radiology and cardiology.
  • Proprietary formats for retinal imaging modalities like OCT and OCTA impede data sharing and analysis.

Purpose of the Study:

  • To review the necessity of harmonized imaging standards in ophthalmology.
  • To detail DICOM standards for retinal imaging modalities (OP, OCT, OCTA) and metadata.
  • To explore the potential of DICOM standardization for advancing AI in ophthalmology.

Main Methods:

  • Review of existing literature on retinal imaging standards and DICOM.
  • Detailed examination of DICOM standards applicable to ophthalmic photography, OCT, and OCTA.
  • Exploration of AI applications and datasets in retinal imaging, focusing on DICOM compliance.

Main Results:

  • DICOM standardization is essential for overcoming data fragmentation in retinal imaging.
  • The AI-Ready and Equitable Atlas for Diabetes Insights (AI-READI) dataset is the first DICOM-compliant multimodal retinal imaging dataset.
  • AI-READI offers a valuable resource for diabetes research, setting a precedent for future standardized datasets.

Conclusions:

  • Adopting DICOM standards in ophthalmology is critical for clinical interoperability and AI advancement.
  • Standardized retinal imaging datasets like AI-READI will accelerate AI-driven breakthroughs in managing retinal diseases, particularly diabetic complications.
  • Further development and adoption of retinal imaging standardization are needed to unlock the full potential of AI in ophthalmology.