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New research · Ophthalmology
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2d
StudyGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026

Impact of image enhancement on grading agreement of plus disease features in retinopathy of prematurity.

Fiammetta Catania, Amandine Barjol, Mathias Gallardo … Thibaut Chapron
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OphthalmologyStudy

Image enhancement raised agreement among graders assessing retinopathy of prematurity plus disease features

Impact of image enhancement on grading agreement of plus disease features in retinopathy of prematurity.

Fiammetta Catania … Thibaut Chapron
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
Background

To determine whether targeted image enhancement improves intergrader agreement in assessing vascular features of plus disease in retinopathy of prematurity (retinopathy of prematurity), and to identify which specific features and clinical subgroups benefit most.

Methods

This prospective image grading study included 48 eyes from preterm infants undergoing retinopathy of prematurity screening at a tertiary care center.

n = 48 eyes
Results

graders agreed more often on plus disease signs with enhanced images

61.5%
Raw
67.8%
Enhanced
More results

The greatest improvements were observed for arterial tortuosity (+ 11.9%) and vein tortuosity (+ 6.5%).

Nasal quadrants showed the most pronounced benefit.

Subgroup analyses revealed larger improvements in borderline pre-plus cases and in eyes that had previously received treatment.

“
Conclusion · 1 of 2

Targeted image enhancement improves intergrader agreement in the evaluation of plus disease in retinopathy of prematurity, especially for diagnostically challenging features and regions.

Conclusion · 2 of 2

The strongest effect is seen in borderline and treated cases, supporting the integration of standardized preprocessing techniques to enhance diagnostic objectivity for both clinicians and AI-assisted systems.

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