title: Automated Quantification of Nonperfusion in Three Retinal Plexuses Using Projection-Resolved Optical Coherence Tomography Angiography in Diabetic Retinopathy.
abstract: OBJECTIVE
  The purpose of this study was to evaluate an automated algorithm for detecting avascular area (AA) in optical coherence tomography angiograms (OCTAs) separated into three individual plexuses using a projection-resolved technique.
  
  
  METHODS
  A 3 × 3 mm macular OCTA was obtained in 13 healthy and 13 mild nonproliferative diabetic retinopathy (NPDR) participants. A projection-resolved algorithm segmented OCTA into three vascular plexuses: superficial, intermediate, and deep. An automated algorithm detected AA in each of the three plexuses that were segmented and in the combined inner-retinal angiograms. We assessed the diagnostic accuracy of extrafoveal and total AA using segmented and combined angiograms, the agreement between automated and manual detection of AA, and the within-visit repeatability.
  
  
  RESULTS
  The sum of extrafoveal AA from the segmented angiograms was larger in the NPDR group by 0.17 mm2 (P < 0.001) and detected NPDR with 94.6% sensitivity (area under the receiver operating characteristic curve [AROC] = 0.99). In the combined inner-retinal angiograms, the extrafoveal AA was larger in the NPDR group by 0.01 mm2 (P = 0.168) and detected NPDR with 26.9% sensitivity (AROC = 0.62). The total AA, inclusive of the foveal avascular zone, in the segmented and combined angiograms, detected NPDR with 23.1% and 7.7% sensitivity, respectively. The agreement between the manual and automated detection of AA had a Jaccard index of >0.8. The pooled SDs of AA were small compared with the difference in mean for control and NPDR groups.
  
  
  CONCLUSIONS
  An algorithm to detect AA in OCTA separated into three individual plexuses using a projection-resolved algorithm accurately distinguishes mild NPDR from control eyes. Automatically detected AA agrees with manual delineation and is highly repeatable.
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States.
authors.forename: Miao
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States.
authors.forename: Thomas S
authors.initials: TS
authors.lastname: Hwang
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States 2College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China.
authors.forename: Changlei
authors.initials: C
authors.lastname: Dongye
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States.
authors.forename: David J
authors.initials: DJ
authors.lastname: Wilson
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States.
authors.forename: David
authors.initials: D
authors.lastname: Huang
authors.affiliation: Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States.
authors.forename: Yali
authors.initials: Y
authors.lastname: Jia
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D005260:Female; D005451:Fluorescein Angiography / Q000379:methods*; D005500:Follow-Up Studies; D005654:Fundus Oculi; D006801:Humans; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000000981:diagnostic imaging*; D041623:Tomography, Optical Coherence / Q000379:methods*