Title: Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,
Abstract: Despite significant advances in artificial intelligence (AI) for computer
vision, its application in medical imaging has been limited by the burden and
limits of expert-generated labels. We used images from optical coherence
tomography angiography (OCTA), a relatively new imaging modality that measures
perfusion of the retinal vasculature, to train an AI algorithm to generate
vasculature maps from standard structural optical coherence tomography (OCT)
images of the same retinae, both exceeding the ability and bypassing the need
for expert labeling. Deep learning was able to infer perfusion of
microvasculature from structural OCT images with similar fidelity to OCTA and
significantly better than expert clinicians (P < 0.00001). OCTA suffers from
need of specialized hardware, laborious acquisition protocols, and motion
artifacts; whereas our model works directly from standard OCT which are
ubiquitous and quick to obtain, and allows unlocking of large volumes of
previously collected standard OCT data both in existing clinical trials and
clinical practice. This finding demonstrates a novel application of AI to
medical imaging, whereby subtle regularities between different modalities are
used to image the same body part and AI is used to generate detailed and
accurate inferences of tissue function from structure imaging.