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.

Complement factor H 402H variant and reticular macular disease.

OBJECTIVE: To determine the association of high-risk alleles in the complement factor H (CFH; Y402H, rs1061170) and age-related maculopathy susceptibility (ARMS2; A69S, rs10490924) genes with reticular macular disease (RMD), a major clinical subphenotype of age-related macular degeneration (AMD). METHODS: Using retinal images from the Columbia Macular Genetics Study, we identified 67 subject individuals with RMD. A comparison group of 64 subjects with AMD without RMD was matched by ethnicity, age, sex, and AMD clinical stage. RESULTS: In the RMD group, 53 of 67 subjects (79.1%) were female, the mean age was 83 years, and 47 of 67 (70.1%) had late AMD, with closely matched values in the non-RMD group. The frequencies of the CFH 402H allele were 39.6% in the RMD group (53 of 134 individuals) and 58.6% in the non-RMD group (75 of 128 individuals) (χ(2) = 8.8; P = .003; odds ratio, 0.46 [95% confidence interval, 0.28-0.76]). The corresponding frequencies of the risk allele for ARMS2 were 44.0% (40 of 128 individuals) and 31.3% (40 of 128 individuals), respectively (χ(2) = 4.0; P = .045; odds ratio, 1.73 [95% confidence interval, 1.04-2.90]). Homozygosity for 402H was particularly associated with the absence of RMD, occurring in 8 of 67 subjects (11.9%) with RMD vs 24 of 64 subjects (37.5%) without RMD (P < .001). Retinal macular disease also was associated with hypertension among male patients. CONCLUSIONS: The AMD-associated CFH 402H risk variant is significantly associated with the absence of RMD but enhanced risk for RMD is conferred by the ARMS2 69S AMD risk allele. These results are consistent with the hypothesis that 402H may confer a survival benefit against certain infections, some of which may cause RMD. CLINICAL RELEVANCE: Reticular macular disease may be genetically distinct from the rest of AMD.