ArticleBiomedical optics express2025
AI-aided segmentation of four types of drusen in volumetric OCT.
Article in Biomedical optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Drusen volume and reticular pseudodrusen volume from optical coherence tomography with deep learning as risk factors for progression to late age-related macular degeneration in eyes with reticular pseudodrusen and contralateral macular neovascularisation.The British journal of ophthalmology · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Drusen are a hallmark biomarker of age-related macular degeneration (AMD), with their size, number, and morphology (type) closely linked to disease severity and progression. Accurate segmentation and classification of drusen from optical coherence tomography (OCT) images are essential for objective AMD assessment and monitoring. In this work, we present a deep learning framework that combines a convolutional neural network for automated drusen segmentation with a dedicated classification module to distinguish four clinically relevant, distinct drusen types based on segmentation output. We evaluated our approach on a comprehensive dataset and achieved a mean Dice score of 0.74 ± 0.21 for voxel-wise segmentation accuracy and a critical success index of 0.69 ± 0.24 for drusen count accuracy. This method demonstrates substantial improvements in the quantitative drusen analysis and offers a promising tool for enhanced AMD diagnosis and tracking of disease progression.
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Registered trials
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