ArticleOphthalmology science2026
Automated Nonperfusion Quantification in Diabetic Retinopathy on Ultra-Widefield Swept-Source OCT Angiography.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Swept-Source Wide-Field OCT and OCTA (24 × 20 mm and 26 × 21 mm) in Inherited Retinal Dystrophies: First Clinical Experience with Two Novel Devices.Journal of clinical medicine · 2026Article
- Evaluation of Vascular Biomarkers in Diabetic Retinopathy Using Ultrawide-Field Swept-Source Optical Coherence Tomography Angiography: The DRIVE Study.Investigative ophthalmology & visual science · 2026Article
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Authors and funding
23 authors.
Funding
Abstract
Purpose: To evaluate the performance of a customized deep learning algorithm for automated segmentation of nonperfusion area (NPA) on ultra-widefield swept-source OCTA (UWF SS-OCTA) and its utility in diabetic retinopathy (DR) severity assessment. Design: Cross-sectional study. Subjects: A total of 180 eyes from 122 participants representing all grades of DR severity. Methods: We developed a convolutional neural network based on a multiscale U-Net backbone with squeeze-and-excitation attention for segmentation of NPAs on en face SS-OCTA all-retinal-layer images from 3 scan patterns: 6 × 6 mm, 12 × 12 mm, and 29 × 24 mm. Ground-truth annotations of NPAs and nongradable area (NGA) on en face OCTA images were generated by 2 independent graders and adjudicated by a vitreoretinal specialist. A corresponding en face structural OCT image was incorporated to distinguish true NPAs from shadow artifacts. Segmentation outputs included NPA, NGA, and shadow artifacts. Pixel-level accuracy was assessed with the F1 score. Nonperfusion index (NPI) was defined as NPA/gradable area. The level of agreement between human-labeled and algorithm-predicted NPI was analyzed using Bland-Altman analysis. Main Outcome Measures: Algorithm F1 score and NPI. Results: The algorithm for NPA segmentation achieved a mean F1 score of 0.82 ± 0.01 in 6 × 6 mm, 0.84 ± 0.03 in 12 × 12 mm, and 0.83 ± 0.02 in 29 × 24 mm, with no significant difference across fields of view ( Conclusions: This deep learning algorithm was validated on single-scan UWF SS-OCTA for automated NPA segmentation and quantification. It demonstrates high accuracy and scalability across multiple scan sizes, supporting its potential integration into objective DR OCTA biomarker analysis. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Registered trials
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