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Automated detection of fundus photographic red lesions in diabetic retinopathy

Larsen, M; Godt, J; Larsen, N; Lund-Andersen, H; Sjolie, AK; Agardh, Elisabet LU ; Kalm, H; Grunkin, M and Owens, DR (2003) In Investigative Ophthalmology & Visual Science 44(2). p.761-766
Abstract
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.... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Investigative Ophthalmology & Visual Science
volume
44
issue
2
pages
761 - 766
publisher
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
external identifiers
  • wos:000180966800044
  • pmid:12556411
  • scopus:0037310006
ISSN
1552-5783
DOI
10.1167/iovs.02-0418
language
English
LU publication?
yes
id
bfe312ff-e445-4a2f-9b1c-d3d360f29a76 (old id 891207)
date added to LUP
2008-01-14 16:17:43
date last changed
2017-07-09 04:29:41
@article{bfe312ff-e445-4a2f-9b1c-d3d360f29a76,
  abstract     = {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.},
  author       = {Larsen, M and Godt, J and Larsen, N and Lund-Andersen, H and Sjolie, AK and Agardh, Elisabet and Kalm, H and Grunkin, M and Owens, DR},
  issn         = {1552-5783},
  language     = {eng},
  number       = {2},
  pages        = {761--766},
  publisher    = {ASSOC RESEARCH VISION OPHTHALMOLOGY INC},
  series       = {Investigative Ophthalmology & Visual Science},
  title        = {Automated detection of fundus photographic red lesions in diabetic retinopathy},
  url          = {http://dx.doi.org/10.1167/iovs.02-0418},
  volume       = {44},
  year         = {2003},
}