We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence.
- Authors
Lee, Cecilia S.; Tyring, Ariel J.; Wu, Yue; Xiao, Sa; Rokem, Ariel S.; DeRuyter, Nicolaas P.; Zhang, Qinqin; Tufail, Adnan; Wang, Ruikang K.; Lee, Aaron Y.
- 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.
- Publication
Scientific Reports, 2019, Vol 9, Issue 1, pN.PAG
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-019-42042-y