title: Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload.
abstract: OBJECTIVE
  To assess the safety and workload reduction of an automated 'disease/no disease' grading system for diabetic retinopathy (DR) within a systematic screening programme.
  
  
  METHODS
  Single 45° macular field image per eye was obtained from consecutive patients attending a regional primary care based DR screening programme in Valencia (Spain). The sensitivity and specificity of automated system operating as 'one or more than one microaneurysm detection for disease presence' grader were determined relative to a manual grading as gold standard. Data on age, gender and diabetes mellitus were also recorded.
  
  
  RESULTS
  A total of 5278 patients with diabetes were screened. The median age and duration of diabetes was 69 years and 6.9 years, respectively. Estimated prevalence of DR was 15.6%. The software classified 43.9% of the patients as having no DR and 26.1% as having ungradable images. Detection of DR was achieved with 94.5% sensitivity (95% CI 92.6- 96.5) and 68.8% specificity (95%CI 67.2-70.4). The overall accuracy of the automated system was 72.5% (95%CI 71.1-73.9).
  
  
  CONCLUSIONS
  The present retinal image processing algorithm that can act as prefilter to flag out images with pathological lesions can be implemented in practice. Our results suggest that it could be considered when implementing DR screening programmes.
authors: European Innovative Biomedicine Institute (EIBI), Cantabria, Spain.
authors: Enrique
authors: E
authors: Soto-Pedre
authors: Amparo
authors: A
authors: Navea
authors: Saray
authors: S
authors: Millan
authors: Maria C
authors: MC
authors: Hernaez-Ortega
authors: Jesús
authors: J
authors: Morales
authors: Maria C
authors: MC
authors: Desco
authors: Pablo
authors: P
authors: Pérez
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D003922:Diabetes Mellitus, Type 1 / Q000150:complications; D003924:Diabetes Mellitus, Type 2 / Q000150:complications; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003941:Diagnostic Techniques, Ophthalmological; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009885:Ophthalmology / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D016526:Workload / Q000706:statistics & numerical data*
keywords: automatic classification; diabetes mellitus; diabetic retinopathy; diagnostic algorithm; digital image; fundus photography; screening; validation