![]() While OCTA can theoretically be obtained using the same OCT hardware, in practice, OCTA requires both hardware and software modifications to existing OCT machines. A recent advance in OCT technology led to its counterpart, OCT angiography (OCTA), which measures blood flow in retinal microvasculature by obtaining repeated measurements of phase and intensity at the same scanning position 2, 3. Since its development in 1991, OCT has become essential in diagnosing and assessing most vision-threatening conditions in ophthalmology 1. Optical coherence tomography (OCT) is a non-invasive imaging modality of structural retina in vivo. 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 inferences of tissue function from structure imaging. Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (Pā<ā0.00001). We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. ![]()
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