Automated detection of proliferative retinopathy in clinical practice

Audrey Karperien1, Herbert F Jelinek1,2, Jorge JG Leandro3, Jo&atilde;o VB Soares3, Roberto M Cesar Jr3, Alan Luckie41School of Community Health, Charles Sturt University, Albury, Australia; 2Centre for Research in Complex Systems, Charles Sturt University, Albury, Australia; 3Creative Visio...

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Autores principales:	Audrey Karperien, Herbert F Jelinek, Jorge JG Leandro, Jo&atilde;o VB Soares, Roberto M Cesar Jr, Alan Luckie
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2008
Materias:	Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/9d5cc592c0774cac834fc84db59c4fac
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spelling	oai:doaj.org-article:9d5cc592c0774cac834fc84db59c4fac2021-12-02T06:48:25ZAutomated detection of proliferative retinopathy in clinical practice1177-54671177-5483https://doaj.org/article/9d5cc592c0774cac834fc84db59c4fac2008-03-01T00:00:00Zhttp://www.dovepress.com/automated-detection-of-proliferative-retinopathy-in-clinical-practice-a347https://doaj.org/toc/1177-5467https://doaj.org/toc/1177-5483Audrey Karperien1, Herbert F Jelinek1,2, Jorge JG Leandro3, Jo&atilde;o VB Soares3, Roberto M Cesar Jr3, Alan Luckie41School of Community Health, Charles Sturt University, Albury, Australia; 2Centre for Research in Complex Systems, Charles Sturt University, Albury, Australia; 3Creative Vision Research Group, Department of Computer Science, IME &ndash; University of S&atilde;o Paulo, Brazil; 4Albury Eye Clinic, Albury, AustraliaAbstract: Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today&rsquo;s world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (Dconn) analysis. The major finding is that compared to other fractal analysis methods, Dconn analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR.Keywords: diabetes, proliferative retinopathy, automated clinical assessment, fractal dimension, complex systems Audrey KarperienHerbert F JelinekJorge JG LeandroJo&atilde;o VB SoaresRoberto M Cesar JrAlan LuckieDove Medical PressarticleOphthalmologyRE1-994ENClinical Ophthalmology, Vol 2008, Iss Issue 1, Pp 109-122 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ophthalmology RE1-994
spellingShingle	Ophthalmology RE1-994 Audrey Karperien Herbert F Jelinek Jorge JG Leandro Jo&atilde;o VB Soares Roberto M Cesar Jr Alan Luckie Automated detection of proliferative retinopathy in clinical practice
description	Audrey Karperien1, Herbert F Jelinek1,2, Jorge JG Leandro3, Jo&atilde;o VB Soares3, Roberto M Cesar Jr3, Alan Luckie41School of Community Health, Charles Sturt University, Albury, Australia; 2Centre for Research in Complex Systems, Charles Sturt University, Albury, Australia; 3Creative Vision Research Group, Department of Computer Science, IME &ndash; University of S&atilde;o Paulo, Brazil; 4Albury Eye Clinic, Albury, AustraliaAbstract: Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today&rsquo;s world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (Dconn) analysis. The major finding is that compared to other fractal analysis methods, Dconn analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR.Keywords: diabetes, proliferative retinopathy, automated clinical assessment, fractal dimension, complex systems
format	article
author	Audrey Karperien Herbert F Jelinek Jorge JG Leandro Jo&atilde;o VB Soares Roberto M Cesar Jr Alan Luckie
author_facet	Audrey Karperien Herbert F Jelinek Jorge JG Leandro Jo&atilde;o VB Soares Roberto M Cesar Jr Alan Luckie
author_sort	Audrey Karperien
title	Automated detection of proliferative retinopathy in clinical practice
title_short	Automated detection of proliferative retinopathy in clinical practice
title_full	Automated detection of proliferative retinopathy in clinical practice
title_fullStr	Automated detection of proliferative retinopathy in clinical practice
title_full_unstemmed	Automated detection of proliferative retinopathy in clinical practice
title_sort	automated detection of proliferative retinopathy in clinical practice
publisher	Dove Medical Press
publishDate	2008
url	https://doaj.org/article/9d5cc592c0774cac834fc84db59c4fac
work_keys_str_mv	AT audreykarperien automateddetectionofproliferativeretinopathyinclinicalpractice AT herbertfjelinek automateddetectionofproliferativeretinopathyinclinicalpractice AT jorgejgleandro automateddetectionofproliferativeretinopathyinclinicalpractice AT joampatildeovbsoares automateddetectionofproliferativeretinopathyinclinicalpractice AT robertomcesarjr automateddetectionofproliferativeretinopathyinclinicalpractice AT alanluckie automateddetectionofproliferativeretinopathyinclinicalpractice
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Automated detection of proliferative retinopathy in clinical practice

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