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

Cecilia S. Lee, Ariel J. Tyring, Yue Wu, Sa Xiao, Ariel S. Rokem, Nicolaas P. Deruyter, Qinqin Zhang, Adnan Tufail, Ruikang K. Wang, Aaron Y. Lee
doi: https://doi.org/10.1101/271346
Cecilia S. Lee
1Department of Ophthalmology, University of Washington, Seattle WA
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Ariel J. Tyring
1Department of Ophthalmology, University of Washington, Seattle WA
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Yue Wu
1Department of Ophthalmology, University of Washington, Seattle WA
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Sa Xiao
1Department of Ophthalmology, University of Washington, Seattle WA
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Ariel S. Rokem
2eScience Institute, University of Washington, Seattle WA
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Nicolaas P. Deruyter
1Department of Ophthalmology, University of Washington, Seattle WA
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Qinqin Zhang
3Department of Bioengineering, University of Washington, Seattle WA
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Adnan Tufail
4Moorfields Eye Hospital NHS Foundation Trust, London UK
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Ruikang K. Wang
1Department of Ophthalmology, University of Washington, Seattle WA
3Department of Bioengineering, University of Washington, Seattle WA
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Aaron Y. Lee
1Department of Ophthalmology, University of Washington, Seattle WA
2eScience Institute, University of Washington, Seattle WA
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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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 25, 2018.
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Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Cecilia S. Lee, Ariel J. Tyring, Yue Wu, Sa Xiao, Ariel S. Rokem, Nicolaas P. Deruyter, Qinqin Zhang, Adnan Tufail, Ruikang K. Wang, Aaron Y. Lee
bioRxiv 271346; doi: https://doi.org/10.1101/271346
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Generating retinal flow maps from structural optical coherence tomography with artificial intelligence
Cecilia S. Lee, Ariel J. Tyring, Yue Wu, Sa Xiao, Ariel S. Rokem, Nicolaas P. Deruyter, Qinqin Zhang, Adnan Tufail, Ruikang K. Wang, Aaron Y. Lee
bioRxiv 271346; doi: https://doi.org/10.1101/271346

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