OBJECTIVE: To gain insight into the pathogenesis of adult-onset foveomacular vitelliform dystrophy (AFVD) via assessment of its pseudohypopyon stage (PHS).
METHODS: Retrospectively, data were collected in a tertiary center from established cohorts of a genetically evaluated AFVD and best vitelliform macular dystrophy (BVMD) eyes in the pseudohypopyon stage. Best-corrected visual acuity (BCVA, LogMAR), lesion characterization, including lesion dimensions, liquefaction areas and patterns (altitudinal or lateral), and ellipsoid zone integrity were analyzed from spectral-domain optical coherence tomography images.
RESULTS: Out of 167 eyes of 90 AFVD patients and 56 eyes of 28 BVMD patients, 8 eyes of six AFVD patients and five eyes of four BVMD patients were at the PHS were included. The mean LogMAR BCVA ± SD was 0.21 ± 0.20 and 0.41 ± 0.10 in AFVD and BVMD diseases, respectively (p = 0.13). Seven AFVD eyes (87.5%) demonstrated lateral liquefaction, while all BVMD eyes demonstrated an altitudinal pattern (p = 0.005). Maximal horizontal lesion diameters were 1.41 ± 0.46 mm and 2.64 ± 0.77 mm in AFVD and BVMD, respectively (p = 0.02). AFVD patients were older (69 ± 14) than BVMD patients (22 ± 13; p = 0.009).
CONCLUSIONS: The pseudohypopyon stage in AFVD is often characterized by a lateral liquefaction pattern, unlike the altitudinal pattern characterizing BVMD. Age, lesion size, or pathogenesis pathways may underline the different pseudohypopyon stage patterns in AFVD and BVMD.

The objective quantification of retinal atrophy associated with age-related macular degeneration (AMD) is required for clinical diagnosis, follow-up, treatment efficacy evaluation, and clinical research. Spectral Domain Optical Coherence Tomography (OCT) has become an essential imaging technology to evaluate the macula. This paper describes a novel automatic method for the identification and quantification of atrophy associated with AMD in OCT scans and its visualization in the corresponding infrared imaging (IR) image. The method is based on the classification of light scattering patterns in vertical pixel-wide columns (A-scans) in OCT slices (B-scans) in which atrophy appears with a custom column-based convolutional neural network (CNN). The network classifies individual columns with 3D column patches formed by adjacent neighboring columns from the volumetric OCT scan. Subsequent atrophy columns form atrophy segments which are then projected onto the IR image and are used to identify and segment atrophy lesions in the IR image and to measure their areas and distances from the fovea. Experimental results on 106 clinical OCT scans (5,207 slices) in which cRORA atrophy (the end point of advanced dry AMD) was identified in 2,952 atrophy segments and 1,046 atrophy lesions yield a mean F1 score of 0.78 (std 0.06) and an AUC of 0.937, both close to the observer variability. Automated computer-based detection and quantification of atrophy associated with AMD using a column-based CNN classification in OCT scans can be performed at expert level and may be a useful clinical decision support and research tool for the diagnosis, follow-up and treatment of retinal degenerations and dystrophies.