ArticleTranslational vision science & technology2026
Validation of a Rule-Based Automated Method for RPE Cell Detection Using Adaptive Optics Transscleral Flood Illumination.
Article in Translational vision science & technology, 2026. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Association Between PS-OCT Derived Polarimetric Entropy and RPE Cellular Metrics Measured With Adaptive Optics Imaging in Retinitis Pigmentosa.Investigative ophthalmology & visual science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to validate a rule-based automated method for detecting retinal pigment epithelium (RPE) cells in adaptive optics (AOs) transscleral flood illumination (AO-TFI) images by comparison with manual annotation and inter-grader agreement. Methods: This cross-sectional study analyzed AO-TFI images from six eyes with retinitis pigmentosa (RP) and five healthy eyes. Regions of interest (ROIs) meeting predefined image quality criteria were selected. After background correction and contrast inversion, RPE cells were detected using a rule-based approach based on local maxima detection and distance filtering. Detection parameters were optimized using 30 ROIs and applied without modification to an independent validation set of 39 ROIs. Automated detections were compared with manual annotations (ground truth [GT]) using nearest-neighbor matching, and Precision, Recall, and F1-score were calculated. Inter-grader agreement between two graders (GT1 versus GT2) was assessed. Results: Automated detection showed stable performance across ROIs. F1-scores ranged from 0.55 to 0.89 in healthy eyes and from 0.70 to 0.92 in RP eyes, with a micro-averaged F1-score of 0.80. In the linear mixed-effects model (LMM), the F1-score of automated detection was significantly lower than inter-grader agreement, and this difference was consistently observed regardless of disease status. F1-scores remained stable across the analyzed signal-to-noise ratio (SNR) range. Conclusions: The rule-based automated detection method applied to AO-TFI images showed lower performance than inter-grader agreement in both healthy and RP eyes, while demonstrating stable detection performance regardless of disease status. This approach represents a proof-of-concept for the feasibility of quantitative analysis of RPE cells using AO-TFI. Further validation using larger cohorts and independent datasets is warranted. Translational Relevance: The automated RPE cell detection method based on AO-TFI evaluated in this study represents an initial step toward standardized quantitative assessment of RPE. With further validation, it may contribute to the development of imaging biomarkers and their application in clinical research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.