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

Purpose: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus auto-fluorescence (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), kappa value, accuracy, and F1 score. Results: The FAF model had an AUC of 0.939 (95% confidence interval [CI], 0.927-0.950), a k 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 k 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 k 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. Published by Elsevier on behalf of the American Academy of Ophthalmology