Automated detection of proliferative retinopathy in clinical practice
Audrey Karperien1, Herbert F Jelinek1,2, Jorge JG Leandro3, João VB Soares3, Roberto M Cesar Jr3, Alan Luckie4
1School 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 – University of São Paulo, Brazil; 4Albury Eye Clinic, Albury, Australia
Abstract: 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’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
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