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Abstract
Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. 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. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data 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 inferences of tissue function from structure imaging.
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Details
1 University of Washington, Department of Ophthalmology, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
2 University of Washington, eScience Institute, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
3 University of Washington, Department of Bioengineering, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
4 Moorfields Eye Hospital NHS Foundation Trust, London, UK (GRID:grid.436474.6) (ISNI:0000 0000 9168 0080)
5 University of Washington, Department of Ophthalmology, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657); University of Washington, Department of Bioengineering, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
6 University of Washington, Department of Ophthalmology, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657); University of Washington, eScience Institute, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)