Prevalence of optic disc haemorrhages in an elderly UK Caucasian population and possible association with reticular pseudodrusen—the Bridlington Eye Assessment Project (BEAP): a cross-sectional study (2002–2006)

Aims: To determine disc haemorrhages (DH) prevalence in an elderly UK population-the Bridlington Eye Assessment Project (BEAP).Methods: Thirty-degree (30°) fundus photographs (3549 participants ≥65 years) were graded for DH/macula changes. Glaucoma evaluation included Goldmann tonometry, 26-point suprathreshold visual-fields and mydriatic slit-lamp assessment for glaucomatous optic neuropathy.Results: 3548 participants with photographs in at least one eye. DH were present in 53 subjects (1.49%), increasing from 1.17% (65-69-year age-group) to 2.19% (80-84-year age53 group), p=0.06. DH was found in 9/96 (9.38%) right eyes (RE) with open angle glaucoma (OAG). Two of twelve RE (16.67%) with normal tension glaucoma (NTG) had DH. Prevalence in eyes without glaucoma was lower (32/3452, [0.93%]). Reticular pseudodrusen (RPD) occurred in 170/3212 (5.29%) subjects without DH, and 8/131 subjects (6.11%) with OAG. Twenty (20) eyes had normal tension glaucoma (NTG), 2 of whom had RPD (10%) (p=0.264). Within a logistic regression model, DH was associated with glaucoma (OR 10.2, 95% CI 5.32 - 19.72) and increasing age (OR 1.05, 95% CI 1.00-1.10, p=0.03). DH was associated with RPD (p=0.05) with univariate analysis but this was not statistically significant in the final adjusted model. There was no significant association with gender, diabetes mellitus (DM), hypertension treatment or AMD grade.Conclusion: DH prevalence is 1.5% in those over 65 years old and significantly associated with glaucoma and increasing age. There appears to be increased RPD prevalence in eyes with DH and NTG with age acting as a confounding factor. Larger studies are required to fully assess the relationship and investigate a possible shared aetiology of choroidal ischaemia

Drusen Analysis in a Human-Machine Synergistic Framework

OBJECTIVES: To demonstrate how human-machine intelligence can be integrated for efficient image analysis of drusen in age-related macular degeneration and to validate the method in 2 large, independently graded, population-based data sets. METHODS: We studied 358 manually graded color slides from the Netherlands Genetic Isolate Study. All slides were digitized and analyzed with a user-interactive drusen detection algorithm for the presence and quantity of small, intermediate, and large drusen. A graphic user interface was used to preprocess the images, choose a region of interest, select appropriate corrective filters for images with photographic artifacts or prominent choroidal pattern, and perform drusen segmentation. Weighted κ statistics were used to analyze the initial concordance between human graders and the drusen detection algorithm; discordant grades from 177 left-eye slides were subjected to exhaustive analysis of causes of disagreement and adjudication. To validate our method further, we analyzed a second data set from our Columbia Macular Genetics Study. RESULTS: The graphical user interface decreased the time required to process images in commercial software by 60.0%. After eliminating borderline size disagreements and applying corrective filters for photographic artifacts and choroidal pattern, the weighted κ values were 0.61, 0.62, and 0.76 for small, intermediate, and large drusen, respectively. Our second data set demonstrated a similarly high concordance. CONCLUSIONS: Drusen identification performed by our user-interactive method presented fair to good agreement with human graders after filters for common sources of error were applied. This approach exploits a synergistic relationship between the intelligent user and machine computational power, enabling fast and accurate quantitative retinal image analysis