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Ferdinand G. Schlanitz, Christian Ahlers, Stefan Sacu, Christopher Schütze, Marcos Rodriguez, Sabine Schriefl, Isabelle Golbaz, Tobias Spalek, Geraldine Stock, Ursula Schmidt-Erfurth; Performance of Drusen Detection by Spectral-Domain Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2010;51(12):6715-6721. doi: 10.1167/iovs.10-5288.
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© ARVO (1962-2015); The Authors (2016-present)
To evaluate the performance of automated analyses integrated in three spectral-domain optical coherence tomography (SD-OCT) devices to identify drusen in eyes with early (i.e., nonatrophic and nonneovascular) age-related macular degeneration (AMD).
Twelve eyes of 12 AMD patients, classified as AREDS 2 and 3 and having a mean count of 113 drusen were examined with three clinical SD-OCT devices (Cirrus [Carl Zeiss Meditec, Dublin CA], 3DOCT-1000 [Topcon, Tokyo, Japan], and Spectralis [Heidelberg Engineering, GmbH, Heidelberg, Germany]) and five different scan patterns. After standard automated segmentation of the RPE was performed, every druse in each B-scan was identified and graded by two independent expert graders. Errors in the segmentation performance were classified as negligible, moderate, or severe. Correlations were based on the diameter and height of the druse and its automated segmentation. The overall drusen pattern identified by experts' detailed delineation was plotted with a custom-made computer program to compare automated to manual identification outcomes.
A total of 1356 drusen were analyzed. The automated segmentation of the retinal pigment epithelium (RPE) by Cirrus made significantly fewer errors in detecting drusen than did the 3DOCT-1000 (P < 0.001). The Cirrus 200 × 200 scan pattern detected 30% of the drusen with negligible errors. Spectralis did not offer a true RPE segmentation. The drusen counts by expert graders were significantly higher in the scans than in the standard fundus photographs (P < 0.05).
SD-OCT imaging proved an excellent performance in visualizing drusen-related RPE disease. However, the available automated segmentation algorithms showed distinct limitations to reliable identification of the amount of drusen, particularly smaller drusen, and the actual size.
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