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

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 algor...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2019-04, Vol.9 (1), p.5694-5694, Article 5694
Main 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
Format: Article
Language:English
Subjects:
Angiography
Artificial Intelligence
Blood flow
Clinical trials
Data processing
Deep Learning
Diabetic Retinopathy - physiopathology
Humans
Medical imaging
Regional Blood Flow
Retina
Retinal Diseases - physiopathology
Retinal Vessels - anatomy & histology
Retinal Vessels - diagnostic imaging
Retinal Vessels - physiology
Retinal Vessels - physiopathology
Structure-function relationships
Tomography
Tomography, Optical Coherence - methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-42042-y