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Despite significant advances in artificial intelligence (AI) for computervision, its application in medical imaging has been limited by the burden andlimits of expert-generated labels. We used images from optical coherencetomography angiography (OCTA), a relatively new imaging modality that measuresperfusion of the retinal vasculature, to train an AI algorithm to generatevasculature maps from standard structural optical coherence tomography (OCT)images of the same retinae, both exceeding the ability and bypassing the needfor expert labeling. Deep learning was able to infer perfusion ofmicrovasculature from structural OCT images with similar fidelity to OCTA andsignificantly better than expert clinicians (P < 0.00001). OCTA suffers fromneed of specialized hardware, laborious acquisition protocols, and motionartifacts; whereas our model works directly from standard OCT which areubiquitous and quick to obtain, and allows unlocking of large volumes ofpreviously collected standard OCT data both in existing clinical trials andclinical practice. This finding demonstrates a novel application of AI tomedical imaging, whereby subtle regularities between different modalities areused to image the same body part and AI is used to generate detailed andaccurate inferences of tissue function from structure imaging.

Generating retinal flow maps from structural optical coherence tomography with artificial intelligence