Evidence map›Paper›PMID 41626424›Full record

ArticleOphthalmology science2026

ReticularNet: Automated Pixel-Level Segmentation of Reticular Pseudodrusen on Near-Infrared Reflectance Images by Deep Learning.

Souvick Mukherjee, Dylan Wu, Leon von der Emde, Emily Vance, Marco Ji, Mehdi Emamverdi, Tharindu De Silva, Alisa T Thavikulwat, Jayashree Kalpathy-Cramer, Amitha Domalpally and 2 more

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Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Souvick MukherjeeDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Dylan WuDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Leon von der EmdeDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Emily VanceDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Marco JiDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Mehdi EmamverdiDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Tharindu De SilvaDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Alisa T ThavikulwatDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Jayashree Kalpathy-CramerDepartment of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
Amitha DomalpallyWisconsin Reading Center, University of Wisconsin-Madison, Madison, Wisconsin.
Catherine A CukrasDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
Tiarnán D L KeenanDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Reticular pseudodrusen (RPD) represent an important biomarker in age-related macular degeneration (AMD) but are difficult to grade and often assessed only for presence or absence, without quantitative or spatial analysis of RPD burden. The objective was to develop and validate a deep learning model for pixel-level RPD grading on near-infrared reflectance (NIR) images, which are commonly acquired in clinical practice and the most accurate en face detection modality. Design: Deep learning model development study. Participants: Five hundred eight images of 117 eyes (70 participants) with or without RPD, over a wide range of AMD severities. Methods: The ground truth grading pipeline comprised reading center multimodal grading for RPD presence and NIR annotation with RPD contours, followed by pixel-level NIR annotation of all individual RPD lesions. The data set was split 80:20 into training and test sets. A DeepLabv3-ResNet-18 segmentation deep learning model ("ReticularNet") was trained to perform pixel-level grading of RPD on NIR images. Its performance was compared with that of 4 ophthalmologists. Main Outcome Measures: Dice similarity coefficient (DSC); intraclass correlation coefficient (ICC) for RPD lesion number, pixel area, and contour area. Results: For pixel-level grading, ReticularNet achieved a mean DSC of 0.36 (standard deviation 0.16). This was significantly higher than the mean DSC of each ophthalmologist (0.03, 0.13, 0.19, and 0.23; Conclusions: ReticularNet achieved automated pixel-level grading of RPD on NIR images. Its grading was superior to that of 4 ophthalmologists, across a variety of metrics. We are making the code/models available for research use. Improved access to quantitative and spatial RPD grading should lead to improved understanding of these lesions as important biomarkers of retinal disease. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Age-related macular degenerationDeep learningNear-infrared reflectance imagingReticular pseudodrusenSubretinal drusenoid deposits

Identifiers

PMID41626424
PMCPMC12856432

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