Automated grading system for evaluation of ocular redness associated with dry eye

John D Rodriguez,1 Patrick R Johnston,1 George W Ousler III,1 Lisa M Smith,1 Mark B Abelson1,21Ora, Inc, Andover, MA, USA; 2Department of Ophthalmology, Harvard Medical School, Boston, MA, USABackground: We have observed that dry eye redness is characterized by a prominence of fine horizontal conjun...

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Autores principales:	Rodriguez JD, Johnston PR, Ousler III GW, Smith LM, Abelson MB
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2013
Materias:	Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/2a3fdeb468ca48fbb4d08b0d77bc2faf
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spelling	oai:doaj.org-article:2a3fdeb468ca48fbb4d08b0d77bc2faf2021-12-02T05:16:13ZAutomated grading system for evaluation of ocular redness associated with dry eye1177-54671177-5483https://doaj.org/article/2a3fdeb468ca48fbb4d08b0d77bc2faf2013-06-01T00:00:00Zhttp://www.dovepress.com/automated-grading-system-for-evaluation-of-ocular-redness-associated-w-a13414https://doaj.org/toc/1177-5467https://doaj.org/toc/1177-5483John D Rodriguez,1 Patrick R Johnston,1 George W Ousler III,1 Lisa M Smith,1 Mark B Abelson1,21Ora, Inc, Andover, MA, USA; 2Department of Ophthalmology, Harvard Medical School, Boston, MA, USABackground: We have observed that dry eye redness is characterized by a prominence of fine horizontal conjunctival vessels in the exposed ocular surface of the interpalpebral fissure, and have incorporated this feature into the grading of redness in clinical studies of dry eye.Aim: To develop an automated method of grading dry eye-associated ocular redness in order to expand on the clinical grading system currently used.Methods: Ninety nine images from 26 dry eye subjects were evaluated by five graders using a 0–4 (in 0.5 increments) dry eye redness (Ora CalibraTM Dry Eye Redness Scale [OCDER]) scale. For the automated method, the Opencv computer vision library was used to develop software for calculating redness and horizontal conjunctival vessels (noted as "horizontality"). From original photograph, the region of interest (ROI) was selected manually using the open source ImageJ software. Total average redness intensity (Com-Red) was calculated as a single channel 8-bit image as R − 0.83G − 0.17B, where R, G and B were the respective intensities of the red, green and blue channels. The location of vessels was detected by normalizing the blue channel and selecting pixels with an intensity of less than 97% of the mean. The horizontal component (Com-Hor) was calculated by the first order Sobel derivative in the vertical direction and the score was calculated as the average blue channel image intensity of this vertical derivative. Pearson correlation coefficients, accuracy and concordance correlation coefficients (CCC) were calculated after regression and standardized regression of the dataset.Results: The agreement (both Pearson's and CCC) among investigators using the OCDER scale was 0.67, while the agreement of investigator to computer was 0.76. A multiple regression using both redness and horizontality improved the agreement CCC from 0.66 and 0.69 to 0.76, demonstrating the contribution of vessel geometry to the overall grade. Computer analysis of a given image has 100% repeatability and zero variability from session to session.Conclusion: This objective means of grading ocular redness in a unified fashion has potential significance as a new clinical endpoint. In comparisons between computer and investigator, computer grading proved to be more reliable than another investigator using the OCDER scale. The best fitting model based on the present sample, and usable for future studies, was C4 = –12.24 + 2.12C2HOR + 0.88C2RED :C4 is the predicted investigator grade, and C2HOR and C2REDare logarithmic transformations of the computer calculated parameters COM-Hor and COM-Red. Considering the superior repeatability, computer automated grading might be preferable to investigator grading in multicentered dry eye studies in which the subtle differences in redness incurred by treatment have been historically difficult to define.Keywords: conjunctival diseases, classification, diagnosis, humans, hyperemia, image processing, computer-assisted, observer variation, keratoconjunctivitis siccaRodriguez JDJohnston PROusler III GWSmith LMAbelson MBDove Medical PressarticleOphthalmologyRE1-994ENClinical Ophthalmology, Vol 2013, Iss default, Pp 1197-1204 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ophthalmology RE1-994
spellingShingle	Ophthalmology RE1-994 Rodriguez JD Johnston PR Ousler III GW Smith LM Abelson MB Automated grading system for evaluation of ocular redness associated with dry eye
description	John D Rodriguez,1 Patrick R Johnston,1 George W Ousler III,1 Lisa M Smith,1 Mark B Abelson1,21Ora, Inc, Andover, MA, USA; 2Department of Ophthalmology, Harvard Medical School, Boston, MA, USABackground: We have observed that dry eye redness is characterized by a prominence of fine horizontal conjunctival vessels in the exposed ocular surface of the interpalpebral fissure, and have incorporated this feature into the grading of redness in clinical studies of dry eye.Aim: To develop an automated method of grading dry eye-associated ocular redness in order to expand on the clinical grading system currently used.Methods: Ninety nine images from 26 dry eye subjects were evaluated by five graders using a 0–4 (in 0.5 increments) dry eye redness (Ora CalibraTM Dry Eye Redness Scale [OCDER]) scale. For the automated method, the Opencv computer vision library was used to develop software for calculating redness and horizontal conjunctival vessels (noted as "horizontality"). From original photograph, the region of interest (ROI) was selected manually using the open source ImageJ software. Total average redness intensity (Com-Red) was calculated as a single channel 8-bit image as R − 0.83G − 0.17B, where R, G and B were the respective intensities of the red, green and blue channels. The location of vessels was detected by normalizing the blue channel and selecting pixels with an intensity of less than 97% of the mean. The horizontal component (Com-Hor) was calculated by the first order Sobel derivative in the vertical direction and the score was calculated as the average blue channel image intensity of this vertical derivative. Pearson correlation coefficients, accuracy and concordance correlation coefficients (CCC) were calculated after regression and standardized regression of the dataset.Results: The agreement (both Pearson's and CCC) among investigators using the OCDER scale was 0.67, while the agreement of investigator to computer was 0.76. A multiple regression using both redness and horizontality improved the agreement CCC from 0.66 and 0.69 to 0.76, demonstrating the contribution of vessel geometry to the overall grade. Computer analysis of a given image has 100% repeatability and zero variability from session to session.Conclusion: This objective means of grading ocular redness in a unified fashion has potential significance as a new clinical endpoint. In comparisons between computer and investigator, computer grading proved to be more reliable than another investigator using the OCDER scale. The best fitting model based on the present sample, and usable for future studies, was C4 = –12.24 + 2.12C2HOR + 0.88C2RED :C4 is the predicted investigator grade, and C2HOR and C2REDare logarithmic transformations of the computer calculated parameters COM-Hor and COM-Red. Considering the superior repeatability, computer automated grading might be preferable to investigator grading in multicentered dry eye studies in which the subtle differences in redness incurred by treatment have been historically difficult to define.Keywords: conjunctival diseases, classification, diagnosis, humans, hyperemia, image processing, computer-assisted, observer variation, keratoconjunctivitis sicca
format	article
author	Rodriguez JD Johnston PR Ousler III GW Smith LM Abelson MB
author_facet	Rodriguez JD Johnston PR Ousler III GW Smith LM Abelson MB
author_sort	Rodriguez JD
title	Automated grading system for evaluation of ocular redness associated with dry eye
title_short	Automated grading system for evaluation of ocular redness associated with dry eye
title_full	Automated grading system for evaluation of ocular redness associated with dry eye
title_fullStr	Automated grading system for evaluation of ocular redness associated with dry eye
title_full_unstemmed	Automated grading system for evaluation of ocular redness associated with dry eye
title_sort	automated grading system for evaluation of ocular redness associated with dry eye
publisher	Dove Medical Press
publishDate	2013
url	https://doaj.org/article/2a3fdeb468ca48fbb4d08b0d77bc2faf
work_keys_str_mv	AT rodriguezjd automatedgradingsystemforevaluationofocularrednessassociatedwithdryeye AT johnstonpr automatedgradingsystemforevaluationofocularrednessassociatedwithdryeye AT ousleriiigw automatedgradingsystemforevaluationofocularrednessassociatedwithdryeye AT smithlm automatedgradingsystemforevaluationofocularrednessassociatedwithdryeye AT abelsonmb automatedgradingsystemforevaluationofocularrednessassociatedwithdryeye
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Automated grading system for evaluation of ocular redness associated with dry eye

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