title: Drusen analysis in a human-machine synergistic framework.
abstract: OBJECTIVE
  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.
authors.affiliation: Columbia University Harkness Eye Institute, 160 Fort Washington Ave, Room 509C, New York, NY 10032, USA. rts1@columbia.edu
authors.forename: R Theodore
authors.initials: RT
authors.lastname: Smith
authors.forename: Mahsa A
authors.initials: MA
authors.lastname: Sohrab
authors.forename: Nicole M
authors.initials: NM
authors.lastname: Pumariega
authors.forename: Kanika
authors.initials: K
authors.lastname: Mathur
authors.forename: Raymond
authors.initials: R
authors.lastname: Haans
authors.forename: Anna
authors.initials: A
authors.lastname: Blonska
authors.forename: Karl
authors.initials: K
authors.lastname: Uy
authors.forename: Dominiek
authors.initials: D
authors.lastname: Despriet
authors.forename: Caroline
authors.initials: C
authors.lastname: Klaver
mesh_terms: D000368:Aged; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008268:Macular Degeneration / Q000175:diagnosis*; D008875:Middle Aged; D010781:Photography; D015203:Reproducibility of Results; D015593:Retinal Drusen / Q000175:diagnosis*; D014584:User-Computer Interface