AI makes retinal imaging 100 times faster, compared to manual method

Good news! Using a twin GAN!

"Researchers at the National Institutes of Health applied artificial intelligence (AI) to a technique that produces high-resolution images of cells in the eye. They report that with AI, imaging is 100 times faster and improves image contrast 3.5-fold. The advance, they say, will provide researchers with a better tool to evaluate age-related macular degeneration (AMD) and other retinal diseases. ...
By integrating AI with AO-OCT [adaptive optics, optical coherence tomography], ... that a major obstacle for routine clinical imaging using AO-OCT has been overcome, especially for diseases that affect the RPE [retinal pigment epithelium], which has traditionally been difficult to image. ..."

From the 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."

AI makes retinal imaging 100 times faster, compared to manual method | National Institutes of Health (NIH) NIH scientists use artificial intelligence called ‘P-GAN’ to improve next-generation imaging of cells in the back of the eye.

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

Credits: Last Week in AI

Fig. 1: Overview of artificial intelligence (AI) enhanced retinal pigment epithelial (RPE) cell imaging strategy.

Fig. 4: Parallel discriminator generative adversarial network (P-GAN) enabled wide-scale visualization of the retinal pigment epithelial (RPE) cellular mosaic.