Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

<div><p>Background</p><p>Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects.</p><p>Methodology</p><p>Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance.</p><p>Principal Findings</p><p>Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables.</p><p>Conclusions and Significance</p><p>We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.</p></div

RF and LR performance using all available eye variables and a subset of eye variables selected by an expert as more clinically relevant.

<p>While this selection led to some improvements for LR it had very little impact on RF performance.</p

Performance across sample sizes of both RF (right panel) and LR is shown for three different scenarios: 1) Only eye data; 2) all variables in the study; and 3) only systemic data.

<p>The addition of systemic variables did not lead to significant increases in classification accuracy.</p

Estimates of RF classification accuracy obtained using the OOB mechanism and two-fold CV.

<p>RF models were estimated using all the available variables.</p

The RF probabilities of having DR were estimated for two groups of participants who were not diagnosed as DR at baseline: a) those who had a DR event (> = 3 step ETDRS progression, vitrectomy, or laser photocoagulation) during follow-up and 2) those who did not.

<p>*Wilcoxon rank sum test, std – standard deviation.</p><p>Estimation was made using baseline data.</p

Diagnosis after four years of follow-up for subjects without DR at baseline, and eye events for each subgroup.

<p>DR events represent changes3 steps in the ETDRS scale during follow-up.</p

Baseline stratification of subjects across DR severity groups and numbers of eye events per group is provided.

<p>DR events represent changes3 steps in the ETDRS scale during follow-up.</p

Most relevant variables according to RF permutation index criterion for each type of data.

<p>The permutation index reflects decreases in classification performance when the values of a given variable have been randomly permuted. Abnormalities refer to the presence of different lesions detected by reviewers (e.g. drusens, age-related macular degeneration features, etc. - see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0098587#pone.0098587.s001" target="_blank">Table S1</a>). ACCORD arm randomization refers to membership to one of the eights arms of the ACCORD trial.</p

Variability in Spectral-Domain Optical Coherence Tomography over 4 Weeks by Age

<p><b><i>Purpose</i></b>: To quantify variation in spectral-domain optical coherence tomography (SD-OCT) measures of total retinal thickness (top of inner limiting membrane to top of retinal pigment epithelium, RPE) and RPE thickness measures over a 4-week period and by age.</p> <p><b><i>Methods</i></b>: A total of 76 volunteers aged 40–85 years were seen at three visits over 4 weeks. Two Topcon SD-OCT scans were taken at each visit. Following grid re-centration, total retinal and RPE thickness were determined in nine subfields. Multilevel modeling was used to quantify variance between scans and by age.</p> <p><b><i>Results</i></b>: In the central circle, mean total retinal thickness was 237.9 µm (standard deviation, SD, 23.5 µm) and RPE thickness was 46.0 µm (SD 5.3 µm). Intraclass correlation coefficient in the central circle was 0.988 for total retinal thickness and 0.714 for RPE thickness. Pairwise measures taken within 4 weeks were strongly correlated (<i>p</i> > 0.95). Within-subject variation of total retinal thickness increased significantly with age. Subjects in the oldest age group had significantly increased among- and within-subject variability in measures of RPE thickness.</p> <p><b><i>Conclusions</i></b>: Correlation between retinal thickness measures was very high (>0.95) over a period of 4 weeks with small changes likely due to variation in measurement. Increasing variability in total retinal and RPE thickness measures with age suggest that the use of more and/or higher quality images to calculate mean thickness to reduce variability may benefit the study of these measures in older persons. This may also impact sample size calculations for future studies of SD-OCT measures in older adults.</p

Mean, standard deviation, and concordance correlation of the inter-grader reproducibility of the EdgeSelect and the Manual methods.

<p>Layers are: inner limiting membrane (ILM), inner segment/ellipsoid interface (ISe), retina/retinal pigment epithelium interface (RPE), and Bruch's membrane (BM).</p