Online citations, reference lists, and bibliographies.
← Back to Search

Comparison Of Diagnosis Of Early Retinal Lesions Of Diabetic Retinopathy Between A Computer System And Human Experts.

S. Lee, E. Lee, R. Kingsley, Y. Wang, D. Russell, R. Klein, A. Warn
Published 2001 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy Visualize in Litmaps
Share
Reduce the time it takes to create your bibliography by a factor of 10 by using the world’s favourite reference manager
Time to take this seriously.
Get Citationsy
OBJECTIVE To investigate whether a computer vision system is comparable with humans in detecting early retinal lesions of diabetic retinopathy using color fundus photographs. METHODS A computer system has been developed using image processing and pattern recognition techniques to detect early lesions of diabetic retinopathy (hemorrhages and microaneurysms, hard exudates, and cotton-wool spots). Color fundus photographs obtained from American Indians in Oklahoma were used in developing and testing the system. A set of 369 color fundus slides were used to train the computer system using 3 diagnostic categories: lesions present, questionable, or absent (Y/Q/N). A different set of 428 slides were used to test and evaluate the system, and its diagnostic results were compared with those of 2 human experts-the grader at the University of Wisconsin Fundus Photograph Reading Center (Madison) and a general ophthalmologist. The experiments included comparisons using 3 (Y/Q/N) and 2 diagnostic categories (Y/N) (questionable cases excluded in the latter). RESULTS In the training phase, the agreement rates, sensitivity, and specificity in detecting the 3 lesions between the retinal specialist and the computer system were all above 90%. The kappa statistics were high (0.75-0.97), indicating excellent agreement between the specialist and the computer system. In the testing phase, the results obtained between the computer system and human experts were consistent with those of the training phase, and they were comparable with those between the human experts. CONCLUSIONS The performance of the computer vision system in diagnosing early retinal lesions was comparable with that of human experts. Therefore, this mobile, electronically easily accessible, and noninvasive computer system, could become a mass screening tool and a clinical aid in diagnosing early lesions of diabetic retinopathy.
This paper references
10.2307/2529186
Statistical Methods for Rates and Proportions.
B. Everitt (1973)
10.2337/diab.29.7.501
A Detailed Study of Risk Factors for Retinopathy and Nephropathy in Diabetes
K. West (1980)
10.1176/AJP.138.12.1644-A
Statistical Methods for Rates and Proportions, 2nd ed
W. Grove (1981)
Electron-opaque inclusions in the rat retinal pigment epithelium after treatment with chelators of zinc.
A. Leure-duPree (1981)
10.2337/diab.32.2.S8
Quantitative Evaluation of Fluorescein Angiograms: Microaneurysm Counts
C. Baudoin (1983)
10.2337/diab.33.1.8
Effect of Diabetes on In Vivo Metabolism of [35S]-labeled Glomerular Basement Membrane
M. Cohen (1984)
10.1016/S0161-6420(84)34336-6
Diabetic retinopathy. Assessment of severity and progression.
B. Klein (1984)
10.1016/S0161-6420(85)34082-4
Comparison between ophthalmoscopy and fundus photography in determining severity of diabetic retinopathy.
S. Moss (1985)
The prevalence and risk of diabetic retinopathy among Indians of southwest Oklahoma.
S. Newell (1989)
10.1001/ARCHOPHT.1989.01070010243030
The Wisconsin Epidemiologic Study of Diabetic Retinopathy. IX. Four-year incidence and progression of diabetic retinopathy when age at diagnosis is less than 30 years.
R. Klein (1989)
Prevalence and incidence of diabetes mellitus--United States, 1980-1987.
(1990)
10.2337/diab.41.3.359
Development of Proliferative Retinopathy in NIDDM: A Follow-up Study of American Indians in Oklahoma
E. Lee (1992)
10.2337/diacare.15.11.1620
Diabetic Retinopathy in Oklahoma Indians With NIDDM: Incidence and risk factors
E. Lee (1992)
10.1001/ARCHOPHT.1993.01090080060019
Comparison of diabetic retinopathy detection by clinical examinations and photograph gradings. Barbados (West Indies) Eye Study Group.
A. Schachat (1993)
10.1016/S0161-6420(93)31449-1
The diagnosis of diabetic retinopathy. Ophthalmoscopy versus fundus photography.
V. Lee (1993)
Vision disorders in diabetes . In : Klein R , Klein BEK , eds
KM West (1995)
Vision disorders in diabetes
R Klein (1995)
10.1136/bjo.80.11.940
Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.
G. G. Gardner (1996)
Stereo photography in the operating theatre.
C. Leverton (1996)
10.1038/eye.1997.166
A fully automated comparative microaneurysm digital detection system
M. Cree (1997)
Screening for early diabetic retinopathy by a high resolution computer vision system
SC Lee (1997)
A real-time high-resolution color imaging system to aid ophthalmologists in diagnosing/grading diabetic retinopathy
SC Lee (1997)
Can digitised color 35mm transparencies be used to diagnose diabetic retinopathy? In: Abstracts of the 16th IDF Congress
LD George (1997)
Digital retinopathy screening
J Prendergast (1997)
10.1111/J.1442-9071.1998.TB01385.X
Tele-ophthalmic screening using digital imaging devices.
K. Yogesan (1998)
10.1002/(SICI)1096-9136(199803)15:3<254::AID-DIA543>3.0.CO;2-C
Instant electronic imaging systems are superior to polaroid at detecting sight‐threatening diabetic retinopathy
R. E. Ryder (1998)
10.1002/(SICI)1096-9136(199802)15:2<170::AID-DIA518>3.0.CO;2-H
Screening for diabetic retinopathy by general practitioners: ophthalmoscopy or retinal photography as 35 mm colour transparencies?
D. Owens (1998)



This paper is referenced by
A Novel Advanced Approach Using Morphological Image Processing Technique for Early Detection of Diabetes Retinopathy
Waheed Sanya (2021)
10.1016/b978-0-12-817438-8.00009-2
Early detection of diabetics using retinal OCT images
M. Ghazal (2020)
10.1016/j.media.2020.101742
A survey on medical image analysis in diabetic retinopathy
S. Stolte (2020)
10.1007/978-981-15-7834-2_22
Analysis of Diabetic Retinopathy Abnormalities Detection Techniques
S. Dandapat (2020)
Hybrid Enhancement Technique of Retinal Fundus Image Using Reconstruction Filter Segmentation with Various Classification and Optimization Methods
N. Sathya (2020)
10.1007/978-3-319-98014-0_9
Innovative Approaches in Delivery of Eye Care: Diabetic Retinopathy
D. Ting (2019)
DIABETIC RETINOPATHY USING MACHINE LEARNING
Manisha Laxman Jadhav (2019)
10.11591/ijeecs.v11.i3.pp%p
Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network
Umadevi Jeyapriya J (2018)
10.1007/s13410-018-0632-3
A critical review of red lesion detection algorithms using fundus images
Shilpa Joshi (2018)
Characterization of Diabetic Retinopathy Detection of Exudates in Color Fundus Images of the Human Retina
K. Parasuraman (2018)
10.18178/IJSPS.5.1.34-38
Grading System for Diabetic Retinopathy Disease
N. Salih (2017)
10.1109/ICCSP.2017.8286715
Filter and fuzzy c means based feature extraction and classification of diabetic retinopathy using support vector machines
A. Roy (2017)
10.1007/s10044-017-0630-y
Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey
T. Soomro (2017)
10.1109/ICIHT.2017.7899006
Automated fundoscopic recognition of diabetic retinopathy features: a review
Ahsan Khawaja (2017)
utomatic detection of microaneurysms in colour fundus mages
.. Jiméneza (2017)
Recommendations for diabetic retinopathy screening
R. Gangwani (2016)
SCENARIO OF DIABETIC RETINOPATHY
T Priya (2016)
10.12809/HKMJ164844
Diabetic retinopathy screening: global and local perspective.
R. Gangwani (2016)
10.1109/iccsp.2016.7754412
Fuzzy C means based feature extraction and classification of diabetic retinopathy using support vector machines
S. Choudhury (2016)
10.1109/GET.2016.7916623
Hybrid system for automatic classification of Diabetic Retinopathy using fundus images
Manasi Purandare (2016)
Title Diabetic retinopathy screening : global and local perspective
R. Gangwani (2016)
10.5120/IJCA2016908834
A Comparative Study on Filters with Special Reference to Retinal Images
H. Kumar (2016)
10.1109/ICIIP.2015.7414756
Novel method for automatic generation of fundus mask
R. Santhakumar (2015)
10.1007/978-81-322-2256-9_9
A New Approach for Color Distorted Region Removal in Diabetic Retinopathy Detection
N. Mukherjee (2015)
10.15171/bi.2015.27
Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy
S. H. Rasta (2015)
Review of Automated Detection for Diabetes Retinopathy Using Fundus Images
R. Maher (2015)
Detection and classification of Non-Proliferative Diabetic Retinopathy using a BackPropagation neural network
Salvador (2015)
Automatic Identification of Varies Stages of Diabetic Retinopathy Using Retinal Fundus Images
R. Maher (2015)
Detection and classification of Non-Proliferative Diabetic Retinopathy using a Back-Propagation neural network Detección y clasificación de Retinopatía Diabética no Proliferativa usando una Red Neuronal de Retropropagación
J. S. Velázquez-González (2015)
Automatic Identification of Optic Disc in Retinal Fundus Images
Shuvayu Goswami (2014)
10.1109/ICDSP.2014.6900707
Projection based algorithm for detecting exudates in color fundus images
C. Eswaran (2014)
10.15623/IJRET.2014.0303115
REVIEW OF METHODS FOR DIABETIC RETINOPATHY DETECTION AND SEVERITY CLASSIFICATION
Madhura Jagannath Paranjpe (2014)
See more
Semantic Scholar Logo Some data provided by SemanticScholar