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Grading Scales of Conjunctival Inflammation.

Anca-Elena Anghelache-Dobrescu¹, Marian-Eduard Toma¹, Radu-Gheorghe Bucșan¹

¹Department of Ophthalmology, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania.

Diagnostics (Basel, Switzerland)

|December 30, 2025

Summary

Assessing conjunctival inflammation is key for ocular diseases. This review covers subjective and objective grading scales, noting objective methods offer better precision for research.

Keywords:

artificial intelligence conjunctival inflammation conjunctival redness digital imaging hyperemia grading scales objective measurement ocular surface disease subjective assessment

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Area of Science:

Ophthalmology
Ocular Surface Disease
Medical Imaging

Background:

Conjunctival inflammation assessment is crucial for diagnosing and monitoring ocular surface diseases.
Existing subjective grading scales, like the CCLRU (Cornea and Contact Lens Research Unit) scale, face limitations such as inter-observer variability.
Technological advancements are driving the development of objective assessment methods.

Purpose of the Study:

To review and summarize available grading scales for conjunctival inflammation.
To compare subjective and objective methodologies for assessing conjunctival inflammation.
To highlight novel developments, including AI-driven approaches.

Main Methods:

Review of existing literature on conjunctival inflammation grading scales.
Discussion of subjective clinical grading systems (e.g., Mandell, Efron, VBR scales).
Exploration of objective methods, including digital imaging and automated algorithms.

Main Results:

Subjective scales provide a standardized framework but lack precision and are prone to variability.
Objective digital imaging systems and AI offer improved reproducibility and sensitivity.
Validation studies show varying reliability for different scales across populations.

Conclusions:

Traditional subjective scales remain clinically relevant for conjunctival inflammation assessment.
Objective measurement systems demonstrate superior repeatability, making them ideal for research.
Future directions involve integrating AI for more precise and automated conjunctival inflammation quantification.