Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2

Ophthalmology. 2020 Dec;127(12):1674-1687. doi: 10.1016/j.ophtha.2020.05.036. Epub 2020 May 21.


Purpose: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD).

Design: Application of deep learning models to the Age-Related Eye Disease Study 2 (AREDS2) dataset.

Participants: FAF and CFP images (n = 11 535) from 2450 AREDS2 participants. Gold standard labels from reading center grading of the FAF images were transferred to the corresponding CFP images.

Methods: A deep learning model was trained to detect RPD in eyes with intermediate to late AMD using FAF images (FAF model). Using label transfer from FAF to CFP images, a deep learning model was trained to detect RPD from CFP (CFP model). Performance was compared with 4 ophthalmologists using a random subset from the full test set.

Main outcome measures: Area under the receiver operating characteristic curve (AUC), κ value, accuracy, and F1 score.

Results: The FAF model had an AUC of 0.939 (95% confidence interval [CI], 0.927-0.950), a κ value of 0.718 (95% CI, 0.685-0.751), and accuracy of 0.899 (95% CI, 0.887-0.911). The CFP model showed equivalent values of 0.832 (95% CI, 0.812-0.851), 0.470 (95% CI, 0.426-0.511), and 0.809 (95% CI, 0.793-0.825), respectively. The FAF model demonstrated superior performance to 4 ophthalmologists, showing a higher κ value of 0.789 (95% CI, 0.675-0.875) versus a range of 0.367 to 0.756 and higher accuracy of 0.937 (95% CI, 0.907-0.963) versus a range of 0.696 to 0.933. The CFP model demonstrated substantially superior performance to 4 ophthalmologists, showing a higher κ value of 0.471 (95% CI, 0.330-0.606) versus a range of 0.105 to 0.180 and higher accuracy of 0.844 (95% CI, 0.798-0.886) versus a range of 0.717 to 0.814.

Conclusions: Deep learning-enabled automated detection of RPD presence from FAF images achieved a high level of accuracy, equal or superior to that of ophthalmologists. Automated RPD detection using CFP achieved a lower accuracy that still surpassed that of ophthalmologists. Deep learning models can assist, and even augment, the detection of this clinically important AMD-associated lesion.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Area Under Curve
  • Datasets as Topic
  • Deep Learning*
  • Female
  • Fluorescein Angiography*
  • Humans
  • Macular Degeneration
  • Male
  • Middle Aged
  • Ophthalmologists
  • Optical Imaging*
  • ROC Curve
  • Reproducibility of Results
  • Retinal Drusen / diagnostic imaging*
  • Sensitivity and Specificity