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(Investigative Ophthalmology and Visual Science. 2003;44:761-766.)
© 2003 by The Association for Research in Vision and Ophthalmology, Inc.
doi:10.1167/iovs.02-0418
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Automated Detection of Fundus Photographic Red Lesions in Diabetic Retinopathy

Michael Larsen,1 Jannik Godt,2 Nicolai Larsen,1 Henrik Lund-Andersen,1 Anne Katrin Sjølie,3 Elisabet Agardh,4 Helle Kalm,5 Michael Grunkin,2 and David R. Owens6

1From the Department of Ophthalmology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark; 2Retinalyze A/S, Hørsholm, Denmark; the 3Department of Ophthalmology, Odense University Hospital, Odense, Denmark; the 4Department of Ophthalmology, Malmö University Hospital, Malmö, Sweden; the 5Department of Ophthalmology, Sahlgrenska University Hospital, Göteborg, Sweden; and the 6Department of Diabetes Research, University of Wales College of Medicine, Cardiff, Wales, United Kingdom.

PURPOSE. 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.




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