Automated machine learning–based classification of proliferative and non-proliferative diabetic reti...
Automated machine learning–based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Purpose
The study aims to classify the eyes with proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR) based on the optical coherence tomography angiography (OCTA) vascular density maps using a supervised machine learning algorithm.
Methods
OCTA vascular density maps (at superficial capillary plexus (SC...
Alternative Titles
Full title
Automated machine learning–based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2709742377
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2709742377
Other Identifiers
ISSN
0721-832X
E-ISSN
1435-702X
DOI
10.1007/s00417-022-05818-z
