Quantification of Early and Intermediate Age-related Macular Degeneration Using OCT “en face” Presentation

Abstract

Background Early and intermediate age-related macular degeneration (AMD) results in drusen deposits under the retinal pigment epithelium (RPE). These early stages of AMD exhibit different risks of progressing to late AMD. To date, early AMD has been classified and quantified by fundus photography. This does not appear to be sensitive enough for clinical trials studying the impact on drusen. SD-OCT with two-dimensional rendering of the segmented slices analysed allows for en face imaging of the drusen. The present trial studied the potential of quantifying early and intermediate AMD by en-face optical coherence tomography (OCT).

Material and Methods Thirty-one eyes of 29 patients in different stages of early and intermediate AMD were studied. To this end, fundus photographs (Kowa VX-10i, Kowa, Tokyo, Japan) and en-face OCT images (RTVue XR Avanti, Optovue, Inc., Fremont, CA, USA) were taken. First, different segmentation levels (6 µm underneath the RPE, on the RPE, 6 µm and 9 µm above the RPE) and different layer thicknesses (5 µm, 10 µm, 20 µm and 30 µm) were analysed to determine the best segmentation for visualising drusen. Drusen were marked manually and their number and surface area calculated. This analysis was then compared with the standardised drusen analyses on fundus photography. Additional changes in early and intermediate AMD such as pigment epithelial detachments (PEDs) and subretinal drusenoid deposits (SDD) as well as small atrophies were also documented and compared.

Outcomes The best segmentation for delineating the drusen on the en-face OCT images was found to be a segmentation 6 µm underneath the RPE with a slice thickness of 20 µm. Comparison of drusen quantification on en-face OCT images with the standardised drusen analysis on fundus photography revealed particularly good similarity. Other changes in early and intermediate AMD, such as PEDs, SDD and small atrophies, were easier to assess on the en-face OCT images.

Conclusions The analysis and quantification of drusen from en-face OCT images with 20 µm segmentation at 6 µm underneath the RPE allows differentiated quantification of various drusen characteristics. Moreover, other changes in early and intermediate AMD can also be analysed. In future observational and clinical trials, this could help quantify drusen.

Key words

early and intermediate AMD - drusen area - number of drusen

• References/Literatur

• 1 Bird AC, Bressler NM, Bressler SB. et al. An international classification and grading system for age-related maculopathy and age-related macular degeneration. Surv Ophthalmol 1995; 39: 367-374 doi:10.1016/s0039-6257(05)80092-x
• 2 Deutsche Ophthalmologische Gesellschaft. Nahrungsergänzungsmittel bei altersabhängiger Makuladegeneration. Aktuelle Stellungnahme der Deutschen Ophthalmologischen Gesellschaft, der Retinologischen Gesellschaft und des Berufsverbandes der Augenärzte Deutschlands (Stand Oktober 2014). Klin Monbl Augenheilkd 2015; 232: 196-201 doi:10.1055/s-0034-1396155
• 3 Ferris FL, Wilkinson CP, Bird A. et al. Clinical classification of age-related macular degeneration. Ophthalmology 2013; 120: 844-851 doi:10.1016/j.ophtha.2012.10.036
• 4 Lim LS, Mitchell P, Seddon JM. et al. Age-related macular degeneration. Lancet 2012; 379: 1728-1738 doi:10.1016/S0140-6736(12)60282-7
• 5 von Strachwitz CN. Trockene altersabhängige Makuladegeneration. Ophthalmologe 2013; 110: 555-565 doi:10.1007/s00347-012-2757-y
• 6 Hartnett ME, Weiter JJ, Garsd A. et al. Classification of retinal pigment epithelial detachments associated with drusen. Graefes Arch Clin Exp Ophthalmol 1992; 230: 11-19 doi:10.1007/bf00166756
• 7 Cukras C, Agrón E, Klein ML. et al. Natural history of drusenoid pigment epithelial detachment in age-related macular degeneration: age-related eye disease study report no. 28. Ophthalmology 2010; 117: 489-499 doi:10.1016/j.ophtha.2009.12.002
• 8 Schmitz-Valckenberg S, Steinberg JS, Fleckenstein M. et al. Combined confocal scanning laser ophthalmoscopy and spectral-domain optical coherence tomography imaging of reticular drusen associated with age-related macular degeneration. Ophthalmology 2010; 117: 1169-1176 doi:10.1016/j.ophtha.2009.10.044
• 9 Fleckenstein M, Schmitz-Valckenberg S, Sunness JS. et al. Geographische Atrophie. In: Holz FG, Pauleikhoff D, Spaide RF, Bird AC. Hrsg. Altersabhängige Makuladegeneration. Berlin, Heidelberg: Springer; 2011: 125-141
• 10 Heimann H, Kellner U. Fundusfotografie und Weitwinkelsysteme. In: Heimann H, Kellner U. Hrsg. Atlas des Augenhintergrundes. Stuttgart: Thieme; 2010
• 11 Kellner S, Rüther K. Prinzipien der Diagnostik retinaler Erkrankungen. Augenheilkunde up2date 2013; 3: 219-237 doi:10.1055/s-0032-1325094
• 12 Nathoo NA, Or C, Young M. et al. Optical coherence tomography-based measurement of drusen load predicts development of advanced age-related macular degeneration. Am J Ophthalmol 2014; 158: 757-761.e1 doi:10.1016/j.ajo.2014.06.021
• 13 Nittala MG, Ruiz-Garcia H, Sadda SR. Accuracy and reproducibility of automated drusen segmentation in eyes with non-neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci 2012; 53: 8319-8324
• 14 Freeman SR, Kozak I, Cheng L. et al. Optical coherence tomography-raster scanning and manual segmentation in determining drusen volume in age-related macular degeneration. Retina 2010; 30: 431-435 doi:10.1097/IAE.0b013e3181bd2f94
• 15 Diniz B, Ribeiro R, Heussen FM. et al. Drusen measurements comparison by fundus photograph manual delineation versus optical coherence tomography retinal pigment epithelial segmentation automated analysis. Retina 2014; 34: 55-62 doi:10.1097/IAE.0b013e31829d0015
• 16 Gregori G, Yehoshua Z, Garcia Filho CA. et al. Change in drusen area over time compared using spectral-domain optical coherence tomography and color fundus imaging change in drusen area over time: SDOCT Versus CFIs. Invest Ophthalmol Vis Sci 2014; 55: 7662-7668
• 17 Yehoshua Z, Gregori G, Sadda SR. et al. Comparison of drusen area detected by spectral domain optical coherence tomography and color fundus imaging. Invest Ophthalmol Vis Sci 2013; 54: 2429-2434
• 18 Kumari K, Mittal D. Drusen quantification for early identification of age related macular degeneration. Adv Image Video Process 2015; 3: 28-40 doi:10.14738/aivp.33.1291
• 19 van Grinsven MJ, Lechanteur YT, van de Ven JP. et al. Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images. Invest Ophthalmol Vis Sci 2013; 54: 3019-3027
• 20 Lee CS, Baughman DM, Lee AY. Deep learning is effective for the classification of OCT images normal versus Age-related Macular Degeneration. Ophthalmol Retina 2017; 1: 322-327 doi:10.1016/j.oret.2016.12.009
• 21 Fang L, Cunefare D, Wang C. et al. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 2017; 8: 2732-2744 doi:10.1364/boe.8.002732
• 22 Venhuizen FG, van Ginneken B, van Asten F. et al. Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography. Invest Ophthalmol Vis Sci 2017; 58: 2318-2328 doi:10.1167/iovs.16-20541
• 23 Schmidt-Erfurth U, Waldstein SM, Klimscha S. et al. Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence. Invest Ophthalmol Vis Sci 2018; 59: 3199-3208 doi:10.1167/iovs.18-24106
• 24 Treder M, Eter N. Chancen von künstlicher Intelligenz und Big Data für die Diagnostik und Behandlung der altersabhängigen Makuladegeneration. Klin Monbl Augenheilkd 2019; 236: 1418-1422 doi:10.1055/a-1012-2036
• 25 Schmidt-Erfurth U, Sadeghipour A, Gerendas BS. et al. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67: 1-29 doi:10.1016/j.preteyeres.2018.07.004
• 26 Burlina P, Pacheco KD, Joshi N. et al. Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 2017; 82: 80-86 doi:10.1016/j.compbiomed.2017.01.018