Evidence map›Paper›PMID 42876516›Full record

ArticleOphthalmology science2026

Automated Segmentation of Retinal Pigment Epithelium Layers in High-Resolution OCT.

Giulia Ritter, Ben Isselmann, Marlene Saßmannshausen, Anna S Jauch, Katharina Wall, Philipp Herrmann, Frank G Holz, Leon von der Emde, Thomas Ach

Abstract read
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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

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

9 authors.

Giulia RitterDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Ben IsselmannDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Marlene SaßmannshausenDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Anna S JauchDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Katharina WallDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Philipp HerrmannDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Frank G HolzDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Leon von der EmdeDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Thomas AchDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To quantify retinal pigment epithelium (RPE) layer thicknesses and reflectivity in healthy participants across different age groups using improved axial, high-resolution (High-Res) OCT. Design: A cross-sectional observational study. Subjects: A total of 110 eyes from 110 healthy participants were included. Participants were categorized into 3 age groups: group 1 (<40 years), group 2 (40-65 years), and group 3 (>65 years). Methods: High-resolution OCT imaging was performed, followed by the segmentation of 3 RPE layers. The inner layer corresponded to RPE layer 1 (High-Res OCT nomenclature) and outer segment interdigitation zone 2, the middle RPE layer corresponded to RPE layers 2 and 3, and the outer RPE layer corresponded to RPE layers 4 and 5 and Bruch membrane. A convolutional neural network (CNN) was trained for automated RPE layer recognition. Volumetric thickness maps and reflectivity values were generated to assess age-related differences across the macula and analyzed using the Kruskal-Wallis and Mann-Whitney Main Outcome Measures: The primary outcome measures were RPE thicknesses and vitreous-normalized reflectivity of the 3 RPE layers across the 3 age groups. Results: The CNN achieved high segmentation accuracy with a mean Dice of 0.85. Hyporeflective RPE layer 1 was absent at the fovea but increased in thickness with eccentricity. Significant age-related increases were observed in the reflectivity throughout the middle hyperreflective RPE layer, at the fovea (H = 13.8; Conclusions: OCT imaging with improved resolution enables detailed segmentation of RPE layers, revealing moderate but significant age-related changes in reflectivity and thickness. These findings provide normative data essential for identifying early retinal alterations and developing structural biomarkers for age-related retinal diseases. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Normative reference mapsRetinal imaging biomarkersThickness mapping

Identifiers

PMID42876516
PMCPMC13661213

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.