Deep Learning‐Based Detection of Reticular Pseudodrusen in Age‐Related Macular Degeneration
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| Format: | Artículo Open Access |
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Wiley
2025
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| author | 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 |
| author_facet | 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 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 |
| collection | Wiley Open Access |
| contents | Deep Learning‐Based Detection of Reticular Pseudodrusen in Age‐Related Macular Degeneration 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 Clinical & Experimental Ophthalmology 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. 10.1111/ceo.14607 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1111/ceo.14607 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_ceo_14607 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Deep Learning‐Based Detection of Reticular Pseudodrusen in Age‐Related Macular Degeneration 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 Clinical & Experimental Ophthalmology Deep Learning‐Based Detection of Reticular Pseudodrusen in Age‐Related Macular Degeneration 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 Clinical & Experimental Ophthalmology 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. 10.1111/ceo.14607 http://creativecommons.org/licenses/by/4.0/ |
| title | Deep Learning‐Based Detection of Reticular Pseudodrusen in Age‐Related Macular Degeneration |
| topic | Clinical & Experimental Ophthalmology |
| url | https://onlinelibrary.wiley.com/doi/10.1111/ceo.14607 |