Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography

Vineeta Das; Furu Zhang; Andrew J Bower; Joanne Li; Tao Liu; Nancy Aguilera; Bruno Alvisio; Zhuolin Liu; Daniel X Hammer; Johnny Tam

doi:10.1038/s43856-024-00483-1

Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography

Commun Med (Lond). 2024 Apr 10;4(1):68. doi: 10.1038/s43856-024-00483-1.

Authors

Affiliations

¹ National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
² Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
³ National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA. johnny@nih.gov.

Abstract

Background: In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput.

Methods: Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study.

Results: We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges.

Conclusions: The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.

Plain language summary

The retinal pigment epithelium (RPE) is a single layer of cells within the eye that is crucial for vision. These cells are unhealthy in many eye diseases, and this can result in vision problems, including blindness. Imaging RPE cells in living human eyes is time consuming and difficult with the current technology. Our method substantially speeds up the process of RPE imaging by incorporating artificial intelligence. This enables larger areas of the eye to be imaged more efficiently. Our method could potentially be used in the future during routine eye tests. This could lead to earlier detection and treatment of eye diseases, and the prevention of some causes of blindness.