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Original Investigation
July 23, 2020

Quantification of Fluid Resolution and Visual Acuity Gain in Patients With Diabetic Macular Edema Using Deep Learning: A Post Hoc Analysis of a Randomized Clinical Trial

Author Affiliations
  • 1Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
  • 2Jaeb Center for Health Research, Tampa, Florida
  • 3Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
JAMA Ophthalmol. 2020;138(9):945-953. doi:10.1001/jamaophthalmol.2020.2457
Key Points

Question  Is it possible to automatically quantify intraretinal and subretinal fluid in eyes with diabetic macular edema using a deep learning algorithm in large optical coherence tomographic data sets and evaluate the associations of anti–vascular endothelial growth factor treatment with intraretinal and subretinal fluid volumetric changes?

Findings  In this post hoc analysis of a randomized clinical trial in which intraretinal and subretinal fluid was quantified using a fully automated algorithm, aflibercept and ranibizumab were associated with a greater reduction of intraretinal fluid than was bevacizumab. No difference among anti–vascular endothelial growth factor agents was observed regarding reduction of subretinal fluid.

Meaning  Automated quantification of intraretinal and subretinal fluid may be an objective approach to assess the effect of treatment for diabetic macular edema.

Abstract

Importance  Large amounts of optical coherence tomographic (OCT) data of diabetic macular edema (DME) are acquired, but many morphologic features have yet to be identified and quantified.

Objective  To examine the volumetric change of intraretinal fluid (IRF) and subretinal fluid (SRF) in DME during anti–vascular endothelial growth factor treatment using deep learning algorithms.

Design, Setting, and Participants  This post hoc analysis of a randomized clinical trial, the Diabetic Retinopathy Clinical Research Network (protocol T), assessed 6945 spectral-domain OCT volume scans of 570 eyes from 570 study participants with DME. The original trial was performed from August 21, 2012, to October 18, 2018. This analysis was performed from December 7, 2017, to January 15, 2020.

Interventions  Participants were treated according to a predefined, standardized protocol with aflibercept, ranibizumab, or bevacizumab with or without deferred laser.

Main Outcomes and Measures  The association of treatment with IRF and SRF volumes and best-corrected visual acuity (BCVA) during 12 months using deep learning algorithms.

Results  Among the 570 study participants (302 [53%] male; 369 [65%] white; mean [SD] age, 43.4 [12.6] years), the mean fluid volumes in the central 3 mm were 448.6 nL (95% CI, 412.3-485.0 nL) of IRF and 36.9 nL (95% CI, 27.0-46.7 nL) of SRF at baseline and 161.2 nL (95% CI, 135.1-187.4 nL) of IRF and 4.4 nL (95% CI, 1.7-7.1 nL) of SRF at 12 months. The presence of SRF at baseline was associated with a worse baseline BCVA Early Treatment Diabetic Retinopathy Study (ETDRS) score of 63.2 (95% CI, 60.2-66.1) (approximate Snellen equivalent of 20/63 [95% CI, 20/50-20/63]) in eyes with SRF vs 66.9 (95% CI, 65.7-68.1) (approximate Snellen equivalent, 20/50 [95% CI, 20/40-20/50]) without SRF (P < .001) and a greater gain in ETDRS score (0.5; 95% CI, 0.3-0.8) every 4 weeks during follow-up in eyes with SRF at baseline vs 0.4 (95% CI, 0.3-0.5) in eyes without SRF at baseline (P = .02) when adjusted for baseline BCVA. Aflibercept was associated with greater reduction of IRF volume compared with bevacizumab after the first injection (difference, 79.8 nL; 95% CI, 5.3-162.5 nL; P < .001) and every 4 weeks thereafter (difference, 10.4 nL; 95% CI, 0.7-20.0 nL; P = .004). Ranibizumab was associated with a greater reduction of IRF after the first injection compared with bevacizumab (difference, 75.2 nL; 95% CI, 1.4-154.7 nL; P < .001).

Conclusions and Relevance  Automated segmentation of fluid in DME revealed that the presence of SRF was associated with lower baseline BCVA but with good response to anti–vascular endothelial growth factor therapy. These automated spectral-domain OCT analyses may be used clinically to assess anatomical change during therapy.

Trial Registration  ClinicalTrials.gov Identifier: NCT01627249.

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