Training and validation of an automated algorithm to differentiate no and minimal diabetic retinopathy from more severe stages in wide‐field images

The combined method of segmentation followed by feature count analysis shows promising results for binary DR classification in Optomap wide-field images without requiring a large dataset for model development.

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Publication Details

Journal
Acta Ophthalmologica
Published
2026-03-09
DOI
https://doi.org/10.1111/aos.70122
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Training and validation of an automated algorithm to differentiate no and minimal diabetic retinopathy from more severe stages in wide‐field images

Lars Morten Skollerud, Thiusius Rajeeth Savarimuthu, Sebastian Dinesen, Jakob Grauslund et al.
Acta Ophthalmologica
Retinal Imaging and Analysis
article

Training and validation of an automated algorithm to differentiate no and minimal diabetic retinopathy from more severe stages in wide‐field images

Lars Morten Skollerud, Thiusius Rajeeth Savarimuthu, Sebastian Dinesen, Jakob Grauslund, Jakob Kristian Andersen
article en

Abstract

The combined method of segmentation followed by feature count analysis shows promising results for binary DR classification in Optomap wide-field images without requiring a large dataset for model development.

Acta Ophthalmologica
Steno Diabetes Centers (DK), University of Southern Denmark (DK), Maersk (Denmark) (DK), University of Oslo (NO), Odense University Hospital (DK), Sykehuset i Vestfold (NO)
Openalex Percentile: Top 30%
Retinal Imaging and Analysis
DOIOpen Access PDFOpenAlex
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