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

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Advanced OCT imaging of the early forms of age-related macular degeneration and its application to current clinical research

Die altersbedingte Makuladegeneration (AMD) ist die führende Erblindungsursache älterer Personen in den Industrienationen.1 Charakteristisch für die frühen Formen der AMD ist das Vorhandensein von Drusen, Ansammlungen extrazellulären Materials unterhalb des retinalen Pigmentepithels (RPE).2,3 Histologische Studien konnten intensive Wechselbeziehungen zwischen Drusen und dem RPE nachweisen, die möglicherweise einen entscheidenden Anteil an der Entwicklung und Progression der AMD besitzen.4, 5 Diese Doktorarbeit setzt sich aus vier Publikationen zusammen. In den ersten beiden wurde die Möglichkeit überprüft, Drusen mithilfe der optischen Kohärenztomographie (OCT) zu detektieren. In den letzten beiden wurde die Morphologie der Retina und ihre Entwicklung durch den Krankheitsverlauf untersucht. Die OCT ist ein relativ neues, nichtinvasives Bildgebungsverfahren, das dreidimensionale Darstellungen zellulärer Strukturen im Mikrometerbereich erlaubt.^ ^Die erste Publikation beschäftigt sich mit der Darstellung von Drusen in OCT-Bildern allgemein sowie deren automatische Erkennung durch Segmentierungsalgorithmen, die auf den Intensitätsbildern der Spectral-Domain-OCT (SD-OCT) basieren, dem aktuellen klinischen Standard. Die zweite Publikation behandelt die Darstellung und Detektierung von Drusen mithilfe der polarisationssensitiven OCT (PS-OCT). Das PS-OCT ist eine Weiterentwicklung der SD-OCT-Technologie, das eine spezifische Darstellung des RPEs erlaubt. Nachdem Drusen das RPE verformen, entstand die Hypothese, dass das PS-OCT eine genauere Segmentierung von Drusen ermöglicht.^ Segmentiert man lediglich die Form der Drusen und berechnet dadurch deren Ausmaß, übersieht man leicht die Vielfalt der morphologischen Typen, die Drusen einnehmen können.6 Die dritte Publikation präsentiert die Ergebnisse einer Untersuchung darüber, inwiefern das PS-OCT in der Lage ist, klar unterscheidbare morphologische Charakteristiken darzustellen, die möglicherweise einen Einfluß auf das Krankheitsgeschehen haben könnten. Die vierte Publikation präsentiert die Untersuchung der Entwicklung von Drusen über die Zeit. Ziel der Arbeiten ist es, klinisch wichtige Erkenntnisse zur Entwicklung der Erkrankung zu erlangen, um präventive, prognostische und therapeutische Fragestellungen unterstützen zu können.Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in those aged over 50 years living in high-income countries.1 The early forms of AMD are characterized by the presence of drusen.2 Drusen are defined as subretinal deposits of extracellular debris, and pathohistological studies could show an intensive interaction between drusen and their overlying retinal pigmentepithelium(RPE), suggesting drusen as an important clinical factor for progression of disease.3-5 This thesis comprises four publications. The first two tested the feasibility of drusen detection using optical coherence tomography (OCT). The third and fourth investigated the retinal morphology and its development during AMD disease using OCT imaging. OCT is a non-invasive imaging technique which enables the depiction of three-dimensional images of transparent structures in a micrometer scale resolution.^ ^The first publication analyzes the capability of OCT to image drusen and the performance of their automatic delineation programs using segmentation algorithms based on the intensity images of spectral-domain OCTs (SD-OCT), which represent the current clinical standard. The second publication investigates the detection of drusen using polarization-sensitive OCT (PS-OCT). The PS-OCT is a technical development of the SD-OCT, capable to delineate the RPE in a specific way. As drusen deform the contour of the RPE layer, the hypothesis is that PS-OCT will provide a more specific segmentation of drusen. However, with delineation of drusen and calculating their extent alone the various morphologic types of drusen are easily missed.6 Therefore, the third paper presents the results of an examination of the capability of the PS-OCT to image distinct morphologic characteristics of drusen, as these might have an influence on the course of disease.^ The fourth paper presents an investigation of the long-term evolution of drusen during disease development. The purpose of the study is to gain clinically important knowledge about disease development using OCT imaging, in order to support preventive, prognostic and therapeutic questions in the course of AMD.submitted by Dr. med. univ. Ferdinand SchlanitzZusammenfassung in deutscher SpracheMedizinische Universität Wien, Diss., 201

SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY IMAGE CONTRAST AND BACKGROUND COLOR SETTINGS INFLUENCE IDENTIFICATION OF RETINAL STRUCTURES.

PURPOSE To evaluate image contrast and color setting on assessment of retinal structures and morphology in spectral-domain optical coherence tomography. METHODS Two hundred and forty-eight Spectralis spectral-domain optical coherence tomography B-scans of 62 patients were analyzed by 4 readers. B-scans were extracted in 4 settings: W + N = white background with black image at normal contrast 9; W + H = white background with black image at maximum contrast 16; B + N = black background with white image at normal contrast 12; B + H = black background with white image at maximum contrast 16. Readers analyzed the images to identify morphologic features. Interreader correlation was calculated. Differences between Fleiss-kappa correlation coefficients were examined using bootstrap method. Any setting with significantly higher correlation coefficient was deemed superior for evaluating specific features. RESULTS Correlation coefficients differed among settings. No single setting was superior for all respective spectral-domain optical coherence tomography parameters (P = 0.3773). Some variables showed no differences among settings. Hard exudates and subretinal fluid were best seen with B + H (κ = 0.46, P = 0.0237 and κ = 0.78, P = 0.002). Microaneurysms were best seen with W + N (κ = 0.56, P = 0.025). Vitreomacular interface, enhanced transmission signal, and epiretinal membrane were best identified using all color/contrast settings together (κ = 0.44, P = 0.042, κ = 0.57, P = 0.01, and κ = 0.62, P ≤ 0.0001). CONCLUSION Contrast and background affect the evaluation of retinal structures on spectral-domain optical coherence tomography images. No single setting was superior for all features, though certain changes were best seen with specific settings

Multi-surface segmentation of OCT images with AMD using sparse high order potentials

In age-related macular degeneration (AMD), the quantification of drusen is important because it is correlated with the evolution of the disease to an advanced stage. Therefore, we propose an algorithm based on a multi-surface framework for the segmentation of the limiting boundaries of drusen: the inner boundary of the retinal pigment epithelium + drusen complex (IRPEDC) and the Bruch's membrane (BM). Several segmentation methods have been considerably successful in segmenting retinal layers of healthy retinas in optical coherence tomography (OCT) images. These methods are successful because they incorporate prior information and regularization. Nonetheless, these factors tend to hinder the segmentation for diseased retinas. The proposed algorithm takes into account the presence of drusen and geographic atrophy (GA) related to AMD by excluding prior information and regularization just valid for healthy regions. However, even with this algorithm, prior information and regularization still cause the oversmoothing of drusen in some locations. Thus, we propose the integration of local shape prior in the form of a sparse high order potentials (SHOPs) into the algorithm to reduce the oversmoothing of drusen. The proposed algorithm was evaluated in a public database. The mean unsigned errors, relative to the average of two experts, for the inner limiting membrane (ILM), IRPEDC and BM were 2.94 +/- 2.69, 5.53 +/- 5.66 and 4.00 +/- 4.00 mu m, respectively. Drusen areas measurements were evaluated, relative to the average of two expert graders, by the mean absolute area difference and overlap ratio, which were 1579.7 +/- 2106.8 mu m(2) and 0.78 +/- 0.11, respectively.This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. The authors would like to thank the questions and suggestions of the anonymous reviewers that helped to improve this document. J.O. wishes to acknowledge the Portuguese funding institution Fundação Calouste Gulbenkian for the Ph.D. Grant (process 126501) and to ENERMETER - Sistemas de Medição, Lda for their support.info:eu-repo/semantics/publishedVersio

Quantification of the therapeutic response of intraretinal, subretinal, and subpigment epithelial compartments in exudative AMD during anti-VEGF therapy

To analyze the functional and morphologic effects of different ranibizumab treatment regimens on retinal and subretinal as well as sub-RPE compartments in neovascular age-related macular degeneration (nAMD) using spectral-domain optical coherence tomography (SD-OCT) and manual segmentation software

British Journal of Ophthalmology / Impact of drusen and drusenoid retinal pigment epithelium elevation size and structure on the integrity of the retinal pigment epithelium layer

Purpose: To evaluate the impact of drusen size and structure on retinal pigment epithelium (RPE) and photoreceptor layers in eyes with early to intermediate age-related macular degeneration (AMD) using polarisation-sensitive optical coherence tomography (OCT). Design: Retrospective investigation of an observational cross-sectional study. Participants: Patients with early to intermediate AMD. Methods: Twenty-five eyes of 25 patients with drusen were imaged with polarisation-sensitive OCT using macular volume scans. Each scan was manually graded for six distinct drusen characteristics and the integrity of both the overlying RPE and photoreceptor layer. The central scan of each single druse, as well as its diameter and location, were selected for statistical calculations. Results: A total number of 5933 individual drusen including their adjacent RPE and photoreceptor layer were evaluated. 41.3% of all drusen demonstrated an intact overlying RPE; in 28.1% the RPE layer was irregular, but continuous. In 30.6%, the RPE layer signal was discontinuous above the area of drusen. The level of RPE alteration was significantly related to shape (p<0.001), internal reflectivity (p<0.001) and homogeneity (p<0.001) of the drusen and their diameter, with a higher probability for larger drusen to have a discontinuous RPE (OR 3.2, p<0.001). The number of drusen showing overlying foci or an altered photoreceptor layer was too small to be conclusive, but showed a trend towards an altered RPE if present. Conclusions: Polarisation-sensitive OCT reveals a correlation between specific drusen characteristics and the integrity of the overlying RPE layer. Drusen diameter and configuration were significantly associated with RPE loss.(VLID)489848

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