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Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.

Anastasiia Rozhyna1,2, Gábor Márk Somfai3,4, Manfredo Atzori1,5

  • 1Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland.

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|August 10, 2024
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Summary

Researchers reviewed publicly available Optical Coherence Tomography (OCT) datasets to aid artificial intelligence (AI) development in ophthalmology. The study identified 23 datasets, highlighting the need for better data sharing and standardization to advance AI-driven medical diagnostics.

Keywords:
OCTdatadata analysisdata sharingdatasetsopen dataoptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) significantly enhances medical diagnostics, especially in analyzing medical images like Optical Coherence Tomography (OCT).
  • AI algorithms require extensive, high-quality, and annotated patient data for effective training and accurate predictions in computer-aided diagnosis.
  • Researchers often encounter challenges in accessing sufficient medical data due to privacy concerns, limited availability, and lack of annotations, hindering AI development in ophthalmology.

Purpose of the Study:

  • To conduct a comprehensive review of publicly accessible retinal OCT datasets.
  • To compile a detailed list of available OCT datasets and their characteristics for researchers.
  • To facilitate data curation and improve AI applications in OCT-based ophthalmic diagnostics.

Main Methods:

  • A systematic search was conducted across multiple repositories including Zenodo, Mendeley Data, MEDLINE, and Google Dataset search.
  • Identified datasets containing retinal OCT images were evaluated for their properties, size, scope, and ground-truth labels.
  • The review focused on open-access datasets to ensure accessibility for the research community.

Main Results:

  • A total of 23 open-access retinal OCT datasets were identified.
  • These datasets exhibit considerable variation in size, content scope, and the quality of ground-truth labels.
  • The findings underscore a critical need for enhanced data-sharing practices and standardized documentation within the field.

Conclusions:

  • Improving the availability and standardization of retinal OCT datasets is essential for advancing AI algorithm development in ophthalmology.
  • Better data resources will support the creation of more robust AI tools for improved diagnostic capabilities.
  • This review serves as a valuable reference to guide researchers in utilizing and developing AI for medical image analysis in ophthalmology.