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Doppler Optical Coherence Tomography of Retinal Circulation
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Optical coherence tomography choroidal enhancement using generative deep learning.

Valentina Bellemo1,2,3, Ankit Kumar Das4, Syna Sreng1,3

  • 1Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore.

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|May 4, 2024
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Summary
This summary is machine-generated.

An artificial intelligence (AI) model enhances spectral-domain optical coherence tomography (SDOCT) images, improving choroidal visualization. This AI solution enables accurate quantitative measurements of choroidal metrics using affordable SDOCT devices.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Spectral-domain optical coherence tomography (SDOCT) is a standard clinical eye imaging technique but has limited penetration depth, hindering choroid visualization.
  • Choroidal visualization is crucial for diagnosing chorioretinal diseases, and while swept-source OCT (SSOCT) offers better visualization, it is expensive and less accessible.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based solution using generative deep learning (DL) to enhance choroidal visualization in SDOCT images.
  • To enable quantitative measurements of choroidal metrics from AI-enhanced SDOCT images, overcoming the limitations of standard SDOCT penetration depth.

Main Methods:

  • A generative deep learning (DL) model was trained using a dataset of 150,784 SDOCT-SSOCT paired images from healthy, glaucoma, and diabetic retinopathy eyes.
  • The model learned to generate synthetically enhanced SDOCT B-scans with improved choroidal visibility by leveraging deep anatomical features from paired images.
  • An independent external test dataset was used to assess the authenticity and quality of the synthetically enhanced SDOCT images, with experts showing poor accuracy in distinguishing real from synthetic images.

Main Results:

  • Synthetically enhanced SDOCT images showed improved choroidal visibility, and experts could not reliably differentiate them from real SSOCT images (47.5% accuracy).
  • Measurements of choroidal thickness, area, volume, and vascularity index from enhanced SDOCT showed high correlations with reference SSOCT measurements (Pearson's r > 0.87).
  • Intra-class correlation values for choroidal metrics were high, indicating excellent agreement between enhanced SDOCT and SSOCT measurements (ICC > 0.93).

Conclusions:

  • The DL generative model successfully created realistic, enhanced SDOCT data indistinguishable from SSOCT images, significantly improving choroidal visualization.
  • This AI technology enables accurate quantitative measurements of choroidal metrics, overcoming the imaging depth constraints of conventional SDOCT.
  • The findings suggest that this AI-driven approach can expand the utility of affordable SDOCT devices for studying the choroid in various ocular conditions.