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Randomized Controlled Trial
. 2026 Jan-Feb;54(1):78-85.
doi: 10.1111/ceo.14607. Epub 2025 Sep 8.

Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration

Himeesh Kumar  1   2 , Yelena Bagdasarova  3 , Scott Song  3 , Doron G Hickey  1 , Amy C Cohn  1 , Mali Okada  1 , Robert P Finger  4   5 , Jan H Terheyden  4 , Ruth E Hogg  6 , Pierre-Henry Gabrielle  7 , Louis Arnould  7   8 , Maxime Jannaud  1 , Xavier Hadoux  1   2 , Peter van Wijngaarden  1   2 , Carla J Abbott  1   2 , Lauren A B Hodgson  1 , Roy Schwartz  9   10 , Adnan Tufail  9 , Emily Y Chew  11 , Cecilia S Lee  3 , Erica L Fletcher  12 , Melanie Bahlo  13   14 , Brendan R E Ansell  13   14 , Alice Pébay  12   15 , Robyn H Guymer  1   2 , Aaron Y Lee  3 , Zhichao Wu  1   2
Affiliations
Randomized Controlled Trial

Deep Learning-Based Detection of Reticular Pseudodrusen in Age-Related Macular Degeneration

Himeesh Kumar et al. Clin Exp Ophthalmol. 2026 Jan-Feb.

Abstract

Background: Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age-related macular degeneration (AMD). This study sought to develop and externally test a deep learning (DL) model to detect RPD on optical coherence tomography (OCT) scans with expert-level performance.

Methods: RPD were manually segmented in 9800 OCT B-scans from individuals enrolled in a multicentre randomised trial. A DL model for instance segmentation of RPD was developed and evaluated against four retinal specialists in an internal test dataset. The primary outcome was the performance of the DL model for detecting RPD in OCT volumes in five external test datasets compared to two retinal specialists.

Results: In an internal test dataset consisting of 250 OCT B-scans, the DL model produced RPD segmentations that had higher agreement with four retinal specialists (Dice similarity coefficient [DSC] = 0.76) than the agreement amongst the specialists (DSC = 0.68; p < 0.001). In the five external test datasets consisting of 1017 eyes from 812 individuals, the DL model detected RPD in OCT volumes with a similar level of performance as two retinal specialists (area under the receiver operator characteristic curve [AUC] = 0.94, 0.95 and 0.96 respectively; p ≥ 0.32).

Conclusions: We present a DL model for automatic detection of RPD with expert-level performance, which could be used to support the clinical management of AMD. This model has been made publicly available to facilitate future research to understand this critical, yet enigmatic, AMD phenotype.

Keywords: age‐related macular degeneration; deep learning; optical coherence tomography; reticular pseudodrusen; retinal drusen.

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Conflict of interest statement

R.P.F. reports personal fees from Alimera, Bayer, Biogen, Böhringer‐Ingelheim, Caterna, Ellex, Novartis, ODOS, Ophtea, ProGenerika and Roche/Genentech and research funding from Biogen, CentreVue and Zeiss, outside of the submitted work. J.H.T. reports research funding from Roche, Novartis, Bayer, Icare, Zeiss and Heidelberg Engineering and personal fees from Novartis and Okko. P.H.G. reports personal fees from Bayer, Horus, Zeiss, Novartis, Roche, Retinsight, Théa and Abbvie/Allergan outside the submitted work. L.A. reports personal fees from Horus, Théa and Abbvie/Allergan outside the submitted work. P.v.W. reports personal fees from Roche/Genentech, Bayer, Novartis and Mylan outside the submitted work. R.S. reports employment with Apellis Pharmaceuticals outside the submitted work. A.T. reports personal fees from 4DMT, Adverum, Annexon, Apellis, Aviceda, Boehringer Ingleheim, Heidelberg Engineering, Iveric Bio, Janssen, Nanoscope, Novartis, OcuTerra, Ocular Therapeutix, Regenxbio and Roche/Genentech outside the submitted work. A.P. reports personal fees from PYC Therapeutics and Cartherics outside the submitted work. R.H.G. reports personal fees from Roche/Genentech, Bayer, Novartis and Apellis, Belite Bio, Ocular Therapeutix, Complement Therapeutics, Boehringer Ingelheim Pharmaceuticals, Character Bioscience, Janssen, AbbVie and Astellas outside the submitted work. All other authors report nothing to disclose.

Figures

FIGURE 1
FIGURE 1
A representative OCT B‐scan from the internal test set is shown with the segmentation output from the deep model (green) and the annotations from the four retinal specialists (red, orange, blue and purple) overlaid to illustrate the inter‐grader and model‐grader agreement.
FIGURE 2
FIGURE 2
Receiver operating characteristic (ROC) curves showing the performance of the deep learning (DL) model and two retinal specialist graders for detecting reticular pseudodrusen (RPD) in OCT volume scans in all external test datasets combined (the area under the ROC curve [AUC] and their 95% confidence intervals [CI] are also presented in parentheses).
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