ArticleComputational and mathematical methods in medicine2016
Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva.
Article in Computational and mathematical methods in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Grading Scales of Conjunctival Inflammation.Diagnostics (Basel, Switzerland) · 2025Review
- Toward automated assessment of conjunctival hyperemia: A semisupervised artificial intelligence approach.Annals of the New York Academy of Sciences · 2025Article
- Advancing Diagnostics with Semi-Automatic Tear Meniscus Central Area Measurement for Aqueous Deficient Dry Eye Discrimination.Medicina (Kaunas, Lithuania) · 2025Article
- Conjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.Translational vision science & technology · 2025Article
- Meibomian gland secretion quality association with ocular parameters in university students during COVID- 19 restrictions.International ophthalmology · 2023Article
- Ocular redness - I: Etiology, pathogenesis, and assessment of conjunctival hyperemia.The ocular surface · 2021Review
- Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization.Annals of translational medicine · 2020Review
- Development and Evaluation of Semiautomated Quantification of Lissamine Green Staining of the Bulbar Conjunctiva From Digital Images.JAMA ophthalmology · 2017Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient's eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctiva.
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