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Deep learning-based detection of reticular pseudodrusen in age-related macular degeneration

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. Met...

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Main Authors: Kumar, Himeesh (Author) , Bagdasarova, Yelena (Author) , Song, Scott (Author) , Hickey, Doron G. (Author) , Cohn, Amy C. (Author) , Okada, Mali (Author) , Finger, Robert P. (Author) , Terheyden, Jan H. (Author) , Hogg, Ruth E. (Author) , Gabrielle, Pierre-Henry (Author) , Arnould, Louis (Author) , Jannaud, Maxime (Author) , Hadoux, Xavier (Author) , van Wijngaarden, Peter (Author) , Abbott, Carla J. (Author) , Hodgson, Lauren A. B. (Author) , Schwartz, Roy (Author) , Tufail, Adnan (Author) , Chew, Emily Y. (Author) , Lee, Cecilia S. (Author) , Fletcher, Erica L. (Author) , Bahlo, Melanie (Author) , Ansell, Brendan R. E. (Author) , Pébay, Alice (Author) , Guymer, Robyn H. (Author) , Lee, Aaron Y. (Author) , Wu, Zhichao (Author)
Format: Article (Journal)
Language:English
Published: 2025
In: Clinical & experimental ophthalmology
Year: 2025, Pages: 1-8
ISSN:1442-9071
DOI:10.1111/ceo.14607
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1111/ceo.14607
Verlag, kostenfrei, Volltext: http://onlinelibrary.wiley.com/doi/abs/10.1111/ceo.14607
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Author Notes:Himeesh Kumar, Yelena Bagdasarova, Scott Song, Doron G. Hickey, Amy C. Cohn, Mali Okada, Robert P. Finger, Jan H. Terheyden, Ruth E. Hogg, Pierre-Henry Gabrielle, Louis Arnould, Maxime Jannaud, Xavier Hadoux, Peter van Wijngaarden, Carla J. Abbott, Lauren A. B. Hodgson, Roy Schwartz, Adnan Tufail, Emily Y. Chew, Cecilia S. Lee, Erica L. Fletcher, Melanie Bahlo, Brendan R.E. Ansell, Alice Pébay, Robyn H. Guymer, Aaron Y. Lee, Zhichao Wu
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Summary: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.
Item Description:Erstmals veröffentlicht: 8. September 2025
Gesehen am 21.10.2025
Physical Description:Online Resource
ISSN:1442-9071
DOI:10.1111/ceo.14607

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