Delineating retinal breaks in ultra-widefield fundus images with a PraNet-based machine learning model Article Swipe
Takuya Takayama
,
Toshiyuki Uto
,
Taiki Tsuge
,
Yusuke Kondo
,
Hironobu Tampo
,
M. Chiba
,
Toshikatsu Kaburaki
,
Yasuo Yanagi
,
Hidenori Takahashi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.01.25332727
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.01.25332727
Background Retinal breaks are critical lesions that can lead to retinal detachment and vision loss if not detected and treated early. Automated and precise delineation of retinal breaks using ultra- widefield fundus (UWF) images remain a significant challenge in ophthalmology. Objective This study aimed to develop and validate a deep learning model based on the PraNet architecture for the accurate delineation of retinal breaks in UWF images, with a particular focus on segmentation performance in retinal break–positive cases. Methods We developed a deep learning segmentation model based on the PraNet architecture. This study utilized a dataset consisting of 8,083 cases and a total of 34,867 UWF images. Of these, 960 images contained retinal breaks, while the remaining 33,907 images did not. The dataset was split into 34,713 images for training, 81 for validation, and 73 for testing. The model was trained and validated on this dataset. Model performance was evaluated using both image-wise segmentation metrics (accuracy, precision, recall, Intersection over Union (IoU), dice score, centroid distance score) and lesion-wise detection metrics (sensitivity, positive predictive value). Results The PraNet-based model achieved an accuracy of 0.996, a precision of 0.635, a recall of 0.756, an IoU of 0.539, a dice score of 0.652, and a centroid distance score of 0.081 for pixel-level detection of retinal breaks. The lesion-wise sensitivity was calculated as 0.885, and the positive predictive value (PPV) was 0.742. Conclusions To our knowledge, this is the first study to present pixel-level localization of retinal breaks using deep learning on UWF images. Our findings demonstrate that the PraNet-based model provides precise and robust pixel-level segmentation of retinal breaks in UWF images. This approach offers a clinically applicable tool for the precise delineation of retinal breaks, with the potential to improve patient outcomes. Future work should focus on external validation across multiple institutions and integration of additional annotation strategies to further enhance model performance and generalizability.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.08.01.25332727
- https://www.medrxiv.org/content/medrxiv/early/2025/08/05/2025.08.01.25332727.full.pdf
- OA Status
- green
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412994428
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4412994428Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.08.01.25332727Digital Object Identifier
- Title
-
Delineating retinal breaks in ultra-widefield fundus images with a PraNet-based machine learning modelWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-05Full publication date if available
- Authors
-
Takuya Takayama, Toshiyuki Uto, Taiki Tsuge, Yusuke Kondo, Hironobu Tampo, M. Chiba, Toshikatsu Kaburaki, Yasuo Yanagi, Hidenori TakahashiList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.08.01.25332727Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2025/08/05/2025.08.01.25332727.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2025/08/05/2025.08.01.25332727.full.pdfDirect OA link when available
- Concepts
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Fundus (uterus), Retinal, Computer science, Artificial intelligence, Ophthalmology, Optometry, MedicineTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| primary_location.is_published | False |
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| primary_location.landing_page_url | https://doi.org/10.1101/2025.08.01.25332727 |
| publication_date | 2025-08-05 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3110220146, https://openalex.org/W2147860717, https://openalex.org/W199633366, https://openalex.org/W2975924622, https://openalex.org/W2793570745, https://openalex.org/W2314257879, https://openalex.org/W2934399013, https://openalex.org/W2903896358, https://openalex.org/W4281639294, https://openalex.org/W3045215429, https://openalex.org/W3092344722, https://openalex.org/W4311715373, https://openalex.org/W4393530881, https://openalex.org/W4281977119, https://openalex.org/W4317869168, https://openalex.org/W4376643967, https://openalex.org/W3013545899, https://openalex.org/W2991326607, https://openalex.org/W4391570707, https://openalex.org/W4324020180 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5032313766 |
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| corresponding_institution_ids | https://openalex.org/I146500386 |
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