Automated detection of fundus photographic red lesions in diabetic retinopathy
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/891207
- author
- Larsen, M ; Godt, J ; Larsen, N ; Lund-Andersen, H ; Sjolie, AK ; Agardh, Elisabet LU ; Kalm, H ; Grunkin, M and Owens, DR
- organization
- publishing date
- 2003
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Investigative Ophthalmology & Visual Science
- volume
- 44
- issue
- 2
- pages
- 761 - 766
- publisher
- Association for Research in Vision and 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
- 2016-04-01 17:09:41
- date last changed
- 2022-05-16 18:43:00
@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 = {{Association for Research in Vision and 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}},
doi = {{10.1167/iovs.02-0418}},
volume = {{44}},
year = {{2003}},
}