title: Automated detection of fundus photographic red lesions in diabetic retinopathy.
abstract: OBJECTIVE
  To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes.
  
  
  METHODS
  Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed.
  
  
  RESULTS
  Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648).
  
  
  CONCLUSIONS
  Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.
authors.affiliation: Department of Ophthalmology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark. mla@dadlnet.dk
authors.forename: Michael
authors.initials: M
authors.lastname: Larsen
authors.forename: Jannik
authors.initials: J
authors.lastname: Godt
authors.forename: Nicolai
authors.initials: N
authors.lastname: Larsen
authors.forename: Henrik
authors.initials: H
authors.lastname: Lund-Andersen
authors.forename: Anne Katrin
authors.initials: AK
authors.lastname: Sjølie
authors.forename: Elisabet
authors.initials: E
authors.lastname: Agardh
authors.forename: Helle
authors.initials: H
authors.lastname: Kalm
authors.forename: Michael
authors.initials: M
authors.lastname: Grunkin
authors.forename: David R
authors.initials: DR
authors.lastname: Owens
mesh_terms: D000465:Algorithms; D000783:Aneurysm / Q000175:diagnosis*; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003941:Diagnostic Techniques, Ophthalmological; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010781:Photography / Q000379:methods*; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D012166:Retinal Hemorrhage / Q000175:diagnosis*; D012171:Retinal Vessels / Q000473:pathology*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity