title: Automated analysis of retinal images for detection of referable diabetic retinopathy.
abstract: OBJECTIVE
  The diagnostic accuracy of computer detection programs has been reported to be comparable to that of specialists and expert readers, but no computer detection programs have been validated in an independent cohort using an internationally recognized diabetic retinopathy (DR) standard.
  
  
  OBJECTIVE
  To determine the sensitivity and specificity of the Iowa Detection Program (IDP) to detect referable diabetic retinopathy (RDR).
  
  
  METHODS
  In primary care DR clinics in France, from January 1, 2005, through December 31, 2010, patients were photographed consecutively, and retinal color images were graded for retinopathy severity according to the International Clinical Diabetic Retinopathy scale and macular edema by 3 masked independent retinal specialists and regraded with adjudication until consensus. The IDP analyzed the same images at a predetermined and fixed set point. We defined RDR as more than mild nonproliferative retinopathy and/or macular edema.
  
  
  METHODS
  A total of 874 people with diabetes at risk for DR.
  
  
  METHODS
  Sensitivity and specificity of the IDP to detect RDR, area under the receiver operating characteristic curve, sensitivity and specificity of the retinal specialists' readings, and mean interobserver difference (κ).
  
  
  RESULTS
  The RDR prevalence was 21.7% (95% CI, 19.0%-24.5%). The IDP sensitivity was 96.8% (95% CI, 94.4%-99.3%) and specificity was 59.4% (95% CI, 55.7%-63.0%), corresponding to 6 of 874 false-negative results (none met treatment criteria). The area under the receiver operating characteristic curve was 0.937 (95% CI, 0.916-0.959). Before adjudication and consensus, the sensitivity/specificity of the retinal specialists were 0.80/0.98, 0.71/1.00, and 0.91/0.95, and the mean intergrader κ was 0.822.
  
  
  CONCLUSIONS
  The IDP has high sensitivity and specificity to detect RDR. Computer analysis of retinal photographs for DR and automated detection of RDR can be implemented safely into the DR screening pipeline, potentially improving access to screening and health care productivity and reducing visual loss through early treatment.
authors.affiliation: Department of Ophthalmology and Visual Sciences, 11205 PFP, University of Iowa Hospital and Clinics, 200 Hawkins Dr, Iowa City, IA 52242, USA. michael-abramoff@uiowa.edu
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
authors.forename: James C
authors.initials: JC
authors.lastname: Folk
authors.forename: Dennis P
authors.initials: DP
authors.lastname: Han
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Walker
authors.forename: David F
authors.initials: DF
authors.lastname: Williams
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Russell
authors.forename: Pascale
authors.initials: P
authors.lastname: Massin
authors.forename: Beatrice
authors.initials: B
authors.lastname: Cochener
authors.forename: Philippe
authors.initials: P
authors.lastname: Gain
authors.forename: Li
authors.initials: L
authors.lastname: Tang
authors.forename: Mathieu
authors.initials: M
authors.lastname: Lamard
authors.forename: Daniela C
authors.initials: DC
authors.lastname: Moga
authors.forename: Gwénolé
authors.initials: G
authors.lastname: Quellec
authors.forename: Meindert
authors.initials: M
authors.lastname: Niemeijer
mesh_terms: D019540:Area Under Curve; D003930:Diabetic Retinopathy / Q000145:classification / Q000175:diagnosis*; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008269:Macular Edema / Q000145:classification / Q000175:diagnosis; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D009885:Ophthalmology / Q000592:standards; D010781:Photography; D011237:Predictive Value of Tests; D012372:ROC Curve; D012017:Referral and Consultation; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012680:Sensitivity and Specificity