Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes

Abstract

BackgroundArtificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy.ResultsWhen exploring the probability as to whether the subject’s eye was healthy (diagnostic condition, Test 1), we found that the structural and optical property features of the outer plexiform layer (OPL) and the complex formed by the ganglion cell and inner plexiform layers (GCL + IPL) provided the highest probability (positive predictive value (PPV) of 91% and 89%, respectively) for the proportion of patients with positive test results (healthy condition) who were correctly diagnosed (Test 1). The true negative, TP and PPV values remained stable despite the different sizes of training data sets (Test 2). The sensitivity, specificity and PPV were greater or close to 0.70 for the retinal nerve fiber layer’s features, photoreceptor outer segments and retinal pigment epithelium when 23 diabetic eyes with mild retinopathy were mixed with 38 diabetic eyes with no retinopathy (Test 3).ConclusionsA Bayesian ANN trained on structural and optical features from optical coherence tomography data can successfully discriminate between healthy and diabetic eyes with and with no retinopathy. The fractal dimension of the OPL and the GCL + IPL complex predicted by the Bayesian radial basis function network provides better diagnostic utility to classify diabetic eyes with mild retinopathy. Moreover, the thickness and fractal dimension parameters of the retinal nerve fiber layer, photoreceptor outer segments and retinal pigment epithelium show promise for the diagnostic classification between diabetic eyes with and with no mild retinopathy.

The Use of Optical Coherence Tomography for the Detection of Early Diabetic Retinopathy

“…Thickness, total reflectance, and fractal dimension of macular retinal layers were used in the analyses. Fractal dimension of the OPL and the GCL + IPL complex classified diabetic eyes with MDR from healthy controls, while the thickness and fractal dimension of the RNFL, photoreceptor outer segments, and RPE seemed to be useful for the classification between diabetic eyes without DR and with mild retinopathy [53].…”

Fast retinal layer segmentation of spectral domain optical coherence tomography images

“…Methods based on graph theory could accurately segment retinal layer boundaries in normal adult eyes, [19][20][21][22][23][24][25][26][27][28][29] but had high computational complexity. Recently, machine learning approaches, including support vector machines, 30 random forest, 31 and Bayesian artificial neural networks, 32 have attracted much attention. As pointed out in Ref.…”

“…5 with TD-OCT for retinal layer of human eyes, 8 with TD-OCT for retinal layers of human eyes), 8 boundaries (Ref. 23 with SD-OCT for retinal layers of human eyes,32 with TD-OCT for retinal layers of diabetic eyes), 9 boundaries (Ref. 24 with SD-OCT for retinal layers of human eyes,31 with SD-OCT for retinal layers of human eyes), 10 boundaries (Ref.…”

Two-dimensional fractal analysis of retinal tissue of healthy and diabetic eyes with optical coherence tomography

“…Sullivan et al utilized the box counting method to calculate the fractal dimension to classify the breast carcinoma [19]. The power spectrum method has been used to perform the fractal analysis on the layered retinal tissue for investigating the diseased tissue in diabetic patients and healthy subjects [5][6][7]17,20]. In those studies, the fractal analysis was performed on each A-scan within each region of interest (ROI).…”

“…By employing the OCT technique, the thickness and volume measurements of the retinal tissue can be obtained from OCT scans. Particularly, the structural alterations revealed by changes in thickness and volume of the cellular layers of the retina can be measured to characterize the neurodegeneration in patients with diabetes [2][3][4][5][6][7]. The most significant retinal pathology caused by diabetes is diabetic retinopathy (DR), which is characterized by blood vessel damage and neurodegenerative changes.…”