The combined findings of conventional imaging and OCTA demonstrate distinctive features of inflammatory lesions and CNV in multifocal choroiditis, allowing an appropriate management of these sight-threatening lesions. However, OCTA alone did not distinguish between active and inactive CNVs and should be integrated into an MMI approach.
PurposeHistologic details of progression routes to geographic atrophy (GA) in AMD are becoming available through optical coherence tomography (OCT). We studied the origins and evolution of an OCT signature called plateau in eyes with GA and suggested a histologic correlate.MethodsSerial eye-tracked OCT scans and multimodal imaging were acquired from eight eyes of seven patients with GA and plateau signatures over a mean follow-up of 7.7 years (range, 3.7–11.6). The histology of unrelated donor eyes with AMD was reviewed.ResultsDrusenoid pigment epithelial detachment (PED) on OCT imaging progressed into wide-based mound-like signatures with flattened apices characterized by a hyporeflective yet heterogeneous interior and an overlying hyperreflective exterior, similar to outer retinal corrugations previously ascribed to persistent basal laminar deposit (BLamD) but larger. These new signatures are described as “plateaus.” An initial increase of the PED volume and hyporeflectivity of its contents was followed by a decrease in PED volume and thinning of an overlying hyperreflective band attributable to the loss of the overlying RPE leaving persistent BLamD. Both imaging and histology revealed persistent BLamD with defects through which gliotic Müller cell processes pass.ConclusionsPlateaus can be traced back to drusenoid PEDs on OCT imaging. We hypothesize that during progressive RPE atrophy, Müller cell extension through focal defects in the residual persistent BLamD may contribute to the heterogeneous internal reflectivity of these entities. The role of Müller cell activation and extension in the pathogenesis of AMD should be explored in future studies.
Purpose To compare the qualitative and quantitative choroidal neovascularization (CNV) changes after antivascular endothelial growth factor (anti-VEGF) therapy in treatment-naïve and treated eyes with age-related macular degeneration (AMD) using optical coherence tomography angiography (OCTA). Methods Consecutive patients with neovascular AMD underwent multimodal imaging, including OCTA (AngioPlex, CIRRUS HD-OCT model 5000; Carl Zeiss Meditec, Inc., Dublin, OH) at baseline and at three monthly follow-up visits. Treatment-naive AMD patients undergoing anti-VEGF loading phase were included in group A, while treated patients were included in group B. Qualitative and quantitative OCTA analyses were performed on outer retina to choriocapillaris (ORCC) slab. CNV size was measured using a free image analysis software (ImageJ, open-source imaging processing software, 2.0.0). Results Twenty-five eyes of 25 patients were enrolled in our study (mean age 78.32 ± 6.8 years): 13 treatment-naïve eyes in group A and 12 treated eyes in group B. While qualitative analysis revealed no significant differences from baseline to follow-up in the two groups, quantitative analysis showed in group A a significant decrease in lesion area (P = 0.023); in group B, no significant change in the lesion area was observed during anti-VEGF therapy (P = 0.93). Conclusion Treatment-naïve and treated eyes with CNV secondary to neovascular AMD respond differently to anti-VEGF therapy. This should be taken into account when using OCTA for CNV follow-up or planning therapeutic strategies.
Background. In recent years, deep learning has been increasingly applied to a vast array of ophthalmological diseases. Inherited retinal diseases (IRD) are rare genetic conditions with a distinctive phenotype on fundus autofluorescence imaging (FAF). Our purpose was to automatically classify different IRDs by means of FAF images using a deep learning algorithm. Methods. In this study, FAF images of patients with retinitis pigmentosa (RP), Best disease (BD), Stargardt disease (STGD), as well as a healthy comparable group were used to train a multilayer deep convolutional neural network (CNN) to differentiate FAF images between each type of IRD and normal FAF. The CNN was trained and validated with 389 FAF images. Established augmentation techniques were used. An Adam optimizer was used for training. For subsequent testing, the built classifiers were then tested with 94 untrained FAF images. Results. For the inherited retinal disease classifiers, global accuracy was 0.95. The precision-recall area under the curve (PRC-AUC) averaged 0.988 for BD, 0.999 for RP, 0.996 for STGD, and 0.989 for healthy controls. Conclusions. This study describes the use of a deep learning-based algorithm to automatically detect and classify inherited retinal disease in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a diagnostic tool and may give relevant information for future therapeutic approaches.
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