Predicting Drusen Regression from OCT in Patients with Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in developed countries. The presence of drusen is the hallmark of early/intermediate AMD, and their sudden regression is strongly associated with the onset of late AMD. In this work we propose a predictive model of drusen regression using optical coherence tomography (OCT) based features. First, a series of automated image analysis steps are applied to segment and characterize individual drusen and their development. Second, from a set of quantitative features, a random forest classifiser is employed to predict the occurrence of individual drusen regression within the following 12 months. The predictive model is trained and evaluated on a longitudinal OCT dataset of 44 eyes from 26 patients using leave-one-patient-out cross-validation. The model achieved an area under the ROC curve of 0.81, with a sensitivity of 0.74 and a specificity of 0.73. The presence of hyperreflective foci and mean drusen signal intensity were found to be the two most important features for the prediction. This preliminary study shows that predicting drusen regression is feasible and is a promising step toward identification of imaging biomarkers of incoming regression

Detection and Differentiation of Intraretinal Hemorrhage in Spectral Domain Optical Coherence Tomography.

PURPOSE The purpose of this study was to classify and detect intraretinal hemorrhage (IRH) in spectral domain optical coherence tomography (SD-OCT). METHODS Initially the presentation of IRH in BRVO-patients in SD-OCT was described by one reader comparing color-fundus (CF) and SD-OCT using dedicated software. Based on these established characteristics, the presence and the severity of IRH in SD-OCT and CF were assessed by two other masked readers and the inter-device and the inter-observer agreement were evaluated. Further the area of IRH was compared. RESULTS About 895 single B-scans of 24 eyes were analyzed. About 61% of SD-OCT scans and 46% of the CF-images were graded for the presence of IRH (concordance: 73%, inter-device agreement: k = 0.5). However, subdivided into previously established severity levels of dense (CF: 21.3% versus SD-OCT: 34.7%, k = 0.2), flame-like (CF: 15.5% versus SD-OCT: 45.5%, k = 0.3), and dot-like (CF: 32% versus SD-OCT: 24.4%, k = 0.2) IRH, the inter-device agreement was weak. The inter-observer agreement was strong with k = 0.9 for SD-OCT and k = 0.8 for CF. The mean area of IRH detected on SD-OCT was significantly greater than on CF (SD-OCT: 11.5 ± 4.3 mm(2) versus CF: 8.1 ± 5.5 mm(2), p = 0.008). CONCLUSIONS IRH seems to be detectable on SD-OCT; however, the previously established severity grading agreed weakly with that assessed by CF

Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging

Purpose: To develop a data-driven interpretable predictive model of incoming drusen regression as a sign of disease activity and identify optical coherence tomography (OCT) biomarkers associated with its risk in intermediate age-related macular degeneration (AMD). Methods: Patients with AMD were observed every 3 months, using Spectralis OCT imaging, for a minimum duration of 12 months and up to a period of 60 months. Segmentation of drusen and the overlying layers was obtained using a graph-theoretic method, and the hyperreflective foci were segmented using a voxel classification method. Automated image analysis steps were then applied to identify and characterize individual drusen at baseline, and their development was monitored at every follow-up visit. Finally, a machine learning method based on a sparse Cox proportional hazard regression was developed to estimate a risk score and predict the incoming regression of individual drusen. Results: The predictive model was trained and evaluated on a longitudinal dataset of 61 eyes from 38 patients using cross-validation. The mean follow-up time was 37.8 13.8 months. A total of 944 drusen were identified at baseline, out of which 249 (26%) regressed during follow-up. The prediction performance was evaluated as area under the curve (AUC) for different time periods. Prediction within the first 2 years achieved an AUC of 0.75. Conclusions: The predictive model proposed in this study represents a promising step toward image-guided prediction of AMD progression. Machine learning is expected to accelerate and contribute to the development of new therapeutics that delay the progression of AMD.(VLID)484328

A longitudinal comparison of spectral-domain optical coherence tomography and fundus autofluorescence in geographic atrophy

Purpose To identify reliable criteria based on spectral-domain optical coherence tomography (SD OCT) to monitor disease progression in geographic atrophy attributable to age-related macular degeneration (AMD) compared with lesion size determination based on fundus autofluorescence (FAF). Design Prospective longitudinal observational study. Methods setting: Institutional. study population: A total of 48 eyes in 24 patients with geographic atrophy. observation procedures: Eyes with geographic atrophy were included and examined at baseline and at months 3, 6, 9, and 12. At each study visit best-corrected visual acuity (BCVA), FAF, and SD OCT imaging were performed. FAF images were analyzed using the region overlay device. Planimetric measurements in SD OCT, including alterations or loss of outer retinal layers and the RPE, as well as choroidal signal enhancement, were performed with the OCT Toolkit. main outcome measures: Areas of interest in patients with geographic atrophy measured from baseline to month 12 by SD OCT compared with the area of atrophy measured by FAF. Results Geographic atrophy lesion size increased from 8.88 mm² to 11.22 mm² based on quantitative FAF evaluation. Linear regression analysis demonstrated that results similar to FAF planimetry for determining lesion progression can be obtained by measuring the areas of outer plexiform layer thinning (adjusted R2 = 0.93), external limiting membrane loss (adjusted R2 = 0.89), or choroidal signal enhancement (R2 = 0.93) by SD OCT. Conclusions SD OCT allows morphologic markers of disease progression to be identified in geographic atrophy and may improve understanding of the pathophysiology of atrophic AMD