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Updated: May 5, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
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Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review.

David Restrepo1,2, Justin Michael Quion1, Frederico Do Carmo Novaes3

  • 1Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.

Seminars in Ophthalmology
|February 9, 2024
PubMed
Summary
This summary is machine-generated.

Publicly available Optical Coherence Tomography (OCT) datasets for artificial intelligence (AI) in ophthalmology are limited and lack diversity. Creating more representative OCT datasets is crucial for equitable AI development in eye care.

Keywords:
Artificial intelligencedatadatasetsdeep learningoptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Ophthalmology relies heavily on imaging for eye assessment.
  • Machine learning and AI are increasingly applied to ophthalmic imaging datasets.
  • Challenges exist in creating and maintaining diverse datasets in ophthalmology.

Purpose of the Study:

  • To identify and compare publicly available Optical Coherence Tomography (OCT) databases for AI applications.
  • To assess the landscape of OCT datasets used in AI research.

Main Methods:

  • A literature review was conducted on OCT and AI articles with publicly accessible datasets.
  • Databases were searched using PubMed, Scopus, and Web of Science.
  • 50 articles were included after full-text analysis, identifying 8 public OCT datasets.

Main Results:

  • Eight publicly available OCT datasets were identified, containing 154,313 images.
  • Data originated from Spectralis, Cirrus HD, Topcon 3D, and Bioptigen devices.
  • Datasets included normal exams, age-related macular degeneration, and diabetic maculopathy; only one dataset had comprehensive demographics, with the USA being the most represented population.

Conclusions:

  • Current public OCT databases for AI applications are limited by non-representative populations and lack of comprehensive demographic data.
  • These limitations hinder equitable AI development in ophthalmology.
  • There is a critical need for the creation and dissemination of more representative OCT datasets to advance AI in eye care.