Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

Invest Ophthalmol Vis Sci. 2019 Mar 1;60(4):852-857. doi: 10.1167/iovs.18-25634.


Purpose: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs).

Methods: Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 μm and 400 μm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs.

Results: The best DL model was able to predict CST ≥ 250 μm and CFT ≥ 250 μm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ≥ 400 μm and CFT ≥ 400 μm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars.

Conclusions: DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Angiogenesis Inhibitors / therapeutic use
  • Deep Learning*
  • Diabetic Retinopathy / diagnostic imaging*
  • Diabetic Retinopathy / drug therapy
  • Diagnostic Techniques, Ophthalmological
  • False Positive Reactions
  • Female
  • Fundus Oculi
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Intravitreal Injections
  • Macula Lutea / pathology*
  • Macular Edema / diagnostic imaging*
  • Macular Edema / drug therapy
  • Male
  • Middle Aged
  • Neural Networks, Computer
  • Photography / methods*
  • Predictive Value of Tests
  • Randomized Controlled Trials as Topic
  • Ranibizumab / therapeutic use
  • Retrospective Studies
  • Sensitivity and Specificity
  • Tomography, Optical Coherence / methods*
  • Vascular Endothelial Growth Factor A / antagonists & inhibitors


  • Angiogenesis Inhibitors
  • VEGFA protein, human
  • Vascular Endothelial Growth Factor A
  • Ranibizumab