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Clinical Sciences |

Comparison of Diagnosis of Early Retinal Lesions of Diabetic Retinopathy Between a Computer System and Human Experts FREE

Samuel C. Lee, PhD; Elisa T. Lee, PhD; Ronald M. Kingsley, MD; Yiming Wang, MS; Dana Russell, MPH; Ronald Klein, MD; Ann Warn, MD
[+] Author Affiliations

From the School of Electrical and Computer Engineering, University of Oklahoma, Norman (Dr S. Lee); the College of Public Health (Drs E. Lee and Wang and Ms Russell) and the Dean McGee Eye Institute and Department of Ophthalmology (Drs Kingsley and Warn), University of Oklahoma Health Sciences Center, Oklahoma City; and the Department of Ophthalmology and Visual Sciences, University of Wisconsin Medical School, Madison (Dr Klein).


Arch Ophthalmol. 2001;119(4):509-515. doi:10.1001/archopht.119.4.509.
Text Size: A A A
Published online

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 κ 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.

Figures in this Article

DIABETIC retinopathy has been identified as one of the leading causes of blindness.1 Persons with diabetic retinopathy are 29 times more likely to become blind than those without diabetes.2 Blindness owing to diabetes costs the US Government and general public $500 million annually.2 However, because diabetic retinopathy at its early stage is usually asymptomatic, an individual with diabetes may not be aware of the potential risk of developing retinopathy and consequently losing his or her vision. Regular retinal examinations are highly recommended by the National Eye Health Education Program of the National Eye Institute (Bethesda, Md). It is especially important among high-risk groups such as the American Indian population, which has a high prevalence and incidence of diabetic retinopathy.36 The high cost of examination and treatment and the shortage of ophthalmologists, especially in rural areas, are prominent factors that hinder patients from obtaining regular examinations. However, a low-cost mobile computer vision system that can make the initial diagnosis of diabetic retinopathy would be helpful to rural and underserved populations. Individuals who are diagnosed by the computer system as having early retinal lesions would be referred to an ophthalmologist or optometrist for further evaluation.

Diabetic retinopathy can be detected using ophthalmoscopy, fluorescein angiography, color fundus instant photographs, color fundus 35-mm slides, or real-time electronic imaging.711 Several studies comparing the image quality and effectiveness of these methods have been published.1217 Recently there have been several articles published on automatic detection and quantification of diabetic retinopathy lesions from fluorescein angiograms.1820 Fluorescein angiograms from individuals with diabetes were digitized for analysis using digital image-processing techniques.18,19 Computer algorithms were used to detect and count microaneurysms present in the fluorescein images.19 The accuracy, speed, and reproducibility of the computer techniques were assessed and compared with those of clinicians using both digitized and analog images. Manual counting procedures of microaneurysms used by the clinicians were laborious, time-consuming, and subject to human error.19 Digitization of fundus images enables the computer to discriminate microaneurysms from other features and to count the number of microaneurysms present. Computers are well suited for the extraction of quantitative information from such images because of their ability to process data in a fast and efficient manner with a high degree of reproducibility.19

Gardner et al21 recently tried to determine if neural networks could detect diabetic features in fundus images and compared the network with an ophthalmologist. They concluded that detection of normal vessels, exudates, and hemorrhages was possible, with success rates dependent on preprocessing and the number of images used in training.

The digital image processing method1820 and the neural network method21,22 are 2 distinct methods used in pattern recognition. The image processing method is suitable for detecting and counting discrete objects such as hemorrhages and microaneurysms (HMA), hard exudates (HE), cotton-wool spots (CWS) (or soft exudates), etc. This method is particularly useful in deriving algorithms for recognizing small, vague objects, or like images in a nonuniformly illuminated medical image, such as the early lesions (HMA, HE, and CWS) of diabetic retinopathy in a color fundus image. On the other hand, the neural network method is suitable for solving pattern recognition problems involving general patterns such as the lesion patterns exhibited in fundus images, and for describing the various stages of the severity of diabetic retinopathy. Thus, the neural network method may be useful in grading diabetic retinopathy but not in detecting individual lesions.

Our computer system was designed to detect and quantify the following early retinal lesions from color fundus photographs: HMA, HE, and CWS. The system, which can be delivered using a diskette or via the Internet, was developed and tested using nearly 800 color fundus photographs obtained from American Indians who participated in the Vision Keepers Project. This article compares the computer system with human experts in the ability to detect these retinal lesions of early diabetic retinopathy.

The computer system (Figure 1)was designed to receive images that include the optic disc and the macula(Figure 2A). First, the image of the retina is digitized. Then the system checks the quality of the image. If the image does not show any retinal information, particularly retinal vessels, the system will not process it. These types of images can be detected by the computer system using color intensity histograms. Based on empirical data, an image that does not exhibit retinal information will have a color intensity histogram exhibiting a narrow-banded graph with a total intensity span lower than 30 on a 0 to 255 scale. These images may be caused by a variety of reasons such as insufficient pupil dilation, cataract, and problems in photograph and film development procedures (eg, patient blinks, effects of eyelashes or tears, severe film defects, and problems with the flash). If the retinal image has an intensity span higher than 30, image processing and pattern recognition techniques are then applied.

Place holder to copy figure label and caption
Figure 1.

The computer system for detecting early diabetic retinopathy. HMA indicates hemorrhages and microaneurysms; HE, hard exudates; and CWS, cotton-wool spots.

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Figure 2.

A, A typical digitized retinal image that contains hemorrhages and microaneurysms (HMA), hard exudates (HE), and cotton-wool spots (CWS). B, The white, green, and blue spots indicate the detected HMA, HE, and CWS, respectively.

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The image processing techniques employed were designed to achieve 3 purposes: image enhancement, noise removal, and image normalization. Following image processing, pattern recognition techniques were developed for recognizing various essential retinal features (the optic disc, macula, retinal background, and retinal blood vessels) and certain lesions of early nonproliferative diabetic retinopathy (HMA, HE, and CWS).

Other lesions such as macular edema, intraretinal microvascular abnormalities, or venous beading were not included. At the end of the diagnostic test, a computerized diagnostic report was provided, which listed all of the lesions(including questionable lesions), their sizes, and locations. The diagnostic report for the image in Figure 2A is shown in Figure 3. The lesions detected by the computer system are color coded and shown in Figure 2B. The diagnosis given by the Wisconsin Reading Center was moderate nonproliferative retinopathy with the presence of HMA, HE, and CWS. The development of the computer system consisted of the training phase and testing phase.

Place holder to copy figure label and caption
Figure 3.

Diagnostic report of the retinal image in Figure 2A. The normal screen coordinate system is defined as the system with its origin located at the upper left corner of the screen, and the x and y axes are horizontal and vertical coordinates, respectively. The screen size is 512 × 512 pixels.O indicates optic disc; M, macula; HMA, hemorrhages and microaneurysms; HE, hard exudates; CWS, cotton-wool spots; S-location, location indicated by the normal screen coordinate system; and M-location, location indicated by the macula-centered coordinate system.

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TRAINING PHASE

A total of 369 color fundus images obtained from American Indians who participated in the Vision Keepers Project were used as the training set to develop the system. The Vision Keepers Project, funded by the National Eye Institute, is an epidemiological study to determine the prevalence and incidence of eye disease in American Indians from Oklahoma. One thousand eighty-seven participants aged 49 to 83 years were examined by the ophthalmologist (A.W.) using a slitlamp biomicroscope with a 78-diopter (D) lens and indirect ophthalmoscopy. Duplicate fundus photographs of at least 1 eye of each of 1080 participants were obtained using a nonmydriatic 45 camera (Canon CR5-45 NM, Canon, Japan) through pharmacologically dilated pupils. Seven participants either refused or did not have fundus photographs taken for various reasons, including eye conditions that made it unsafe to dilate. Of the 1080 participants, fundus photographs were obtained for both eyes of 1052 participants and for 1 eye of 28 participants. The 2132 fundus photographs were sent to the University of Wisconsin Fundus Photograph Reading Center, Madison, for grading according to a modification of the Airlie House Classification Scheme.2326 For 1973 of these photographs (92.5%), the entire field was considered gradable. One hundred four fundus slides (4.9%) could not be graded. Of the 2132 fundus slides processed by the computer system, 159 (7.5%) were rejected because of poor quality. Overall, 15.7% of the eyes had some form of retinopathy according to the ophthalmologist's diagnosis. Photographs used in this study contained both the optic disc and the macula, a field between Standard Fields numbers#1 and #2.8 Any Standard Field or a combination of them are acceptable by the computer system.

A sample of 369 photographs was selected with the constraint that approximately 50% of the slides would have early lesions of nonproliferative diabetic retinopathy, and the quality of these slides would be considered gradable by the Wisconsin Reading Center and acceptable by the computer system. These color retinal images were processed by the computer system to detect HMA, HE, and CWS. The sample size of 369 slides gave an 87% power at a significance level of .05 to detect a total disagreement rate of 10% with a 5% difference between the disagreement rates in the off-diagonal cells in the 3 × 3 or 2 × 2 tables.27

The system was refined by trial and error based on a retinal specialist's(R.M.K.) diagnosis (lesions present, questionable, or absent [Y/Q/N]). To determine whether the computer system was sufficiently trained, we first compared the diagnostic results of the retinal specialist with those of the computer system. If there was close agreement, we then compared the computer system with the grader and the ophthalmologist and compared these 2 human experts with the retinal specialist. If the agreement rates, sensitivity, specificity, and κ statistics28 between the computer system and human experts were comparable with those obtained between the human experts (grader vs retinal specialist and general ophthalmologist vs retinal specialist), we concluded that the computer system had been well trained.

TESTING PHASE

A different set of 428 color fundus images (referred to as the testing set) were processed and examined by the computer system and its diagnostic results were compared with those made by the grader and the ophthalmologist. These testing slides were also obtained from the Vision Keeper participants with the same constraints as for the training slides.

In addition to the Y/Q/N diagnostic categories, we also made comparisons using just 2 categories (Y/N), excluding questionable lesions. We excluded the questionable category for 2 reasons. First, the ophthalmologist participating in this project did not use the questionable category in her diagnosis. Second, even though the grader used the questionable category, as did the retinal specialist, their agreement rates on questionable lesions were very low. Among the 369 slides, the grader diagnosed 31 questionable cases (including HMA, HE, and CWS), and the retinal specialist diagnosed 64 questionable cases. Only 2 cases were diagnosed as questionable by both experts. The reasons for having a low agreement rate between the retinal specialist and the grader were (1) Different visual equipment might have been used to review the slides; therefore, differences in image size and quality might have affected the visibility of the lesions. (2) For vague lesions, the signal-to-noise ratio is low. To recognize a weak signal in a noisy environment is difficult, if not impossible, for humans and computers. (3) Somewhat different diagnostic criteria were used for questionable lesions.

For these reasons, it was also necessary to compare diagnostic agreement rates excluding questionable lesions. In addition to examining the agreement rates, κ statistics, sensitivity, and specificity were computed for each comparison for the classification of Y/N. Values of κ higher than 0.4 indicate moderate agreement, and values higher than 0.75 indicate excellent agreement.28

TRAINING PHASE

Table 1 presents the comparison between the computer system and the retinal specialist in diagnosing HMA, HE, and CWS, using Y/Q/N and Y/N. Eight of 369 training slides were considered nongradable by the specialist. The numbers in shaded cells are the questionable lesions diagnosed by either the specialist or the computer system. These questionable lesions were excluded when the Y/N classification was used. The agreement rates for detecting the 3 lesions between the computer system and the retinal specialist were excellent (Table 2). Sensitivity and specificity of the computer system were both high (range, 97%-100%). Therefore, we considered that the computer system was well trained in diagnosing the 3 early retinal lesions.

Table Graphic Jump LocationTable 1. Comparison Between Diagnoses Made by the Computer System and by the Retinal Specialist, the Grader, and the General Ophthamologist*
Table Graphic Jump LocationTable 2. Statistical Analyses Comparing the Computer System With the Human Experts*

Results from comparing diagnoses among the retinal specialist, the grader, and the general ophthalmologist, and between the computer system and the same 2 human experts are presented in Table 3. Results showed that, when compared with the grader and ophthalmologist, the agreement rates of the computer system were comparable with those of the retinal specialist. Similarly, when using results from either the grader or ophthalmologist as gold standards, the sensitivity and specificity of the computer system were comparable with or exceeded those of the retinal specialist. Thus, it was concluded that the computer system was well trained.

Table Graphic Jump LocationTable 3. Comparisons Between the Computer System and Human Experts and Between the Human Experts (Training Phase)*
TESTING PHASE

The results from the testing set of images were high as well (Table 1 and Table 2). Using 2 human experts as standards, the computer system achieved high levels of sensitivity and specificity. It was clear from the comparisons that the agreement rates and κ statistics were both higher when the classification consisted of only Y/N categories.

We developed a computer vision system designed to detect signs of early diabetic retinopathy. The system processed 2132 fundus slides that were also sent to the Wisconsin Reading Center for grading. The computer system rejected 159 slides due to poor quality while the Wisconsin Reading Center rejected 104 slides. The computer system was first trained using a set of 369 color fundus photographs. An additional set of 428 slides was then used to compare diagnoses made by the computer system with those made by a grader and a general ophthalmologist. The quality of these photographs was considered acceptable by both the grader and the computer system. In general, our experiments showed that the computer system was able to process all the photographs that the Wisconsin Reading Center considered gradable.

The 3 lesions (HMA, HE, and CWS) were chosen because the current computer system was developed to detect signs of early diabetic retinopathy. Work is still in progress to improve its detectability of these 3 lesions and to expand its capability to detect and quantify more severe lesions such as intraretinal microvascular abnormalities, venous beading, loops, and new retinal vessels.

A 78-D lens and indirect ophthalmoscope were used by the ophthalmologist to examine the patients. The only limitation of the computer system is that it does not have a good 3-dimensional stereoscopic view and may miss retinal thickening, which is not an issue in this article. It is not known whether there would be any difference in the results if a contact lens were used to detect these lesions, although most retinal experts agree that detection of any retinal lesion would be more accurate with a contact lens than a 78-D lens, even though these differences would be greater for macular edema compared with the lesions that were measured in this study.

Since the computer system was trained using the retinal specialist's suggestions, it was expected that the agreement rates and other statistics between the retinal specialist and the computer system would be higher than those between the computer system and the other 2 human experts. Besides the interobserver and intraobserver differences and different criteria used among human experts in diagnosing the 3 lesions, it was confirmed that the grader and the ophthalmologist often used the presence of other lesions in different fields to diagnose a specific lesion. This approach was not used in the algorithm of the computer system. However, the differences were not substantial.

The sensitivity and specificity of the computer system when using the retinal specialist's diagnosis as a standard ranged from 97% to 100%, showing the potential of the computer system to serve as a screening tool. When using the other human experts as the standard, the computer system was less sensitive, while specificity remained high (range, 93%-100%). This demonstrated that the computer system had a small false-positive rate between 0% and 7%.

The computer vision system is capable of learning the diagnostic decision-making process of a human expert with a range of accuracy between 91% and 94% when the 3 diagnostic categories are used, and with a range of accuracy between 97% and 99% when the questionable lesions are excluded. At this time, since we do not have a standard set of criteria in diagnosing questionable lesions, the computer system has no standard criteria to follow. Therefore, the sensitivity and agreement rates between human experts and the computer system, as well as between human experts, are severely affected by the percentage of questionable lesions diagnosed by either examiner. It is expected that the higher the percentage of questionable lesions, the lower the agreement rate.

The agreement rates, sensitivity, and specificity between the computer system and the human experts are comparable with those between human experts in detecting early retinal lesions. The system is mobile, electronically easily accessible (can be delivered by diskette or via the Internet), and noninvasive. It displays an enlarged image on the computer screen along with the immediate diagnostic report (only a few seconds are needed). For these reasons, this system could become a useful clinical aid and a mass screening tool (when connected to a real-time fundus camera) if other clinically important lesions such as macular edema, intraretinal microvascular abnormalities, and venous beading can be detected. This would be especially useful in areas where there is a lack of ophthalmologists. Further research is needed.

The University of Oklahoma hopes to make this computer system commercially available. While it probably will not replace routine eye care, it may be used as a clinical aid by displaying retinal images with enhanced details and facilitating diagnosis. With further development of the system, it is possible that it could be used to detect progression of retinopathy in clinical trials and epidemiological studies. We strongly recommend that a set of standard diagnostic criteria be established for computer detection and quantification of retinal lesions, particularly the questionable lesions.

Accepted for publication September 27, 2000.

This study was supported by grant U10EY09898 from the National Eye Institute, Bethesda, Md.

We wish to thank the Vision Keepers Project participants for allowing us to use their fundus photographs in this study and the reviewers of this article for their comments and suggestions. We also thank Michelle Roberts, BA, and Cathy Morales for their assistance.

Corresponding author and reprints: Samuel C. Lee, PhD, School of Electrical and Computer Engineering, University of Oklahoma, 202 W Boyd, Norman, OK 73019 (e-mail: samlee@ou.edu).

Centers for Disease Control and Prevention, Prevalence and incidence of diabetes mellitus in United States, 1980-1987. MMWR Morb Mortal Wkly Rep. 1990;39809- 812
Klein  RKlein  BEK Vision disorders in diabetes. Klein  RKlein  BEKeds. Diabetes in America 2nd Bethesda, Md: National Institutes of Health and National Institute of Diabetes and Digestive and Kidney Diseases 1995;293- 339
West  KMErdreich  LJStober  JA A detailed study of risk factors for retinopathy and nephropathy in diabetes. Diabetes. 1980;29501- 508
Link to Article
Newell  SETolbert  BBennett  JParsley  TL The prevalence and risk of diabetic retinopathy among Indians of southwest Oklahoma. J Okla State Med Assoc. 1989;82414- 424
Lee  ETLee  VSKingsley  RM  et al.  Diabetic retinopathy in Oklahoma Indians with NIDDM. Diabetes Care. 1992;151620- 1627
Link to Article
Lee  ETLee  VSLum  MRussell  D Development of proliferative retinopathy in NIDDM: a Follow-up Study of American Indians in Oklahoma. Diabetes. 1992;41359- 367
Link to Article
Leverton  C Grading diabetic retinopathy from a computer screen. J Audiov Media Med. 1996;19176
Lee  SC A real-time high-resolution color imaging system to aid ophthalmologists in diagnosing/grading diabetic retinopathy. Diabetes Abstract Book, 57th Annual Meeting and Scientific Sessions. 46suppl 1 Alexandria, Va Journal of the American DiabetesAssociation1997;334A
Lee  SC Screening for early diabetic retinopathy by a high resolution computer vision system.  Abstracts of the 16th IDF Congress July 20-25, 1997 Helsinki, FinlandA497Diabetologia 1997;40suppl 1I- VIA1- A722
Ryder  REKong  NBates  AS  et al.  Instant electronic imaging systems are superior to Polaroid at detecting sight-threatening diabetic retinopathy. Diabet Med. 1998;15254- 258
Link to Article
Yogesan  KConstable  IJEikelboom  RH  et al.  Tele-ophthalmic screening using digital imaging devices. Aust N Z J Ophthalmol. 1998;26suppl 1S9- S11
Link to Article
Moss  SEKlein  RKessler  SD  et al.  Comparison between ophthalmoscopy and fundus photographs in determining severity of diabetic retinopathy. Ophthalmology. 1985;9262- 67
Link to Article
Schachat  APHyman  LLeske  MC  et al.  Comparison of diabetic retinopathy detection by clinical examinations and photograph gradings: Barbados (West Indies) Eye Study Group. Arch Ophthalmol. 1993;1111064- 1070
Link to Article
Lee  VSKingsley  RMLee  ET  et al.  The diagnosis of diabetic retinopathy. Ophthalmology. 1993;1001504- 1512
Link to Article
George  LDLeverton  CYoung  SLusty  JDunstan  FDOwens  DR Can digitised color 35mm transparencies be used to diagnose diabetic retinopathy?  Abstracts of the 16th IDF Congress July 20-25, 1997 Helsinki, FinlandA496Diabetologia 1997;40suppl 1I- VIA1- A722
Link to Article
Prendergast  JDorsey  CMayes  P Digital retinopathy screening. Diabetes Abstract Book, 57th Annual Meeting and Scientific Sessions46suppl 1 Alexandria, Va Journal of the American Diabetes Association1997;341A
Owens  DRGibbons  RLLewis  PAWall  SAllen  JCMorton  R Screening for diabetic retinopathy by general practitioners: ophthalmoscopy or retinal photography as 35 mm colour transparencies? Diabet Med. 1998;15170- 175
Link to Article
Spencer  TPhillips  RPSharp  PFForrester  J Automated detection and quantification of microaneurysms in fluorescein angiograms. Graefs Arch Clin Exp Ophthalmol. 1992;23036- 41
Link to Article
Baudoin  CEManeschi  FQuentel  G  et al.  Quantitative evaluation of fluorescein angiograms: microaneurysm counts. Diabetes. 1984;33suppl 28- 13
Link to Article
Cree  MJOlson  JAMcHardy  KCSharp  PFForrester  JV A fully automated comparative microaneurysm digital detection system. Eye. 1997;11622- 628
Link to Article
Gardner  GGKeating  DWilliamson  THElliot  AT Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol. 1996;80940- 944
Link to Article
Aleynikov  SMicheli-Tzanakou  E Classification of retinal damage by a neural network based system. J Med Syst. 1998;22129- 136
Link to Article
Early Treatment Diabetic Retinopathy Study Research Group (ETDRS), Manual of Operation.  Baltimore ETDRS Coordinating Center, University of Maryland School of Medicine1985;chaps 12 and 18.
Klein  BEKDavis  MDSegal  P  et al.  Diabetic retinopathy: assessment of severity and progression. Ophthalmology. 1984;9110- 17
Link to Article
Klein  RKlein  BEKMoss  SEDaris  MDDeMets  DL 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. Arch Ophthalmol. 1989;107237- 243
Link to Article
Diabetic Retinopathy Study, Report number 6: design, methods, and baseline results: report number 7: a modification of the Airlie House classification of diabetic retinopathy. Invest Ophthalmol Vis Sci. 1981;21 (1) pt 21- 226
Desu  MMRaghavarao  D Sample Size Methodology.  Boston, Mass Academic Press1990;
Fleiss  JL Statistical Methods for Rates and Proportions. 2nd New York, NY John Wiley & Sons1981;

Figures

Place holder to copy figure label and caption
Figure 1.

The computer system for detecting early diabetic retinopathy. HMA indicates hemorrhages and microaneurysms; HE, hard exudates; and CWS, cotton-wool spots.

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Place holder to copy figure label and caption
Figure 2.

A, A typical digitized retinal image that contains hemorrhages and microaneurysms (HMA), hard exudates (HE), and cotton-wool spots (CWS). B, The white, green, and blue spots indicate the detected HMA, HE, and CWS, respectively.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Diagnostic report of the retinal image in Figure 2A. The normal screen coordinate system is defined as the system with its origin located at the upper left corner of the screen, and the x and y axes are horizontal and vertical coordinates, respectively. The screen size is 512 × 512 pixels.O indicates optic disc; M, macula; HMA, hemorrhages and microaneurysms; HE, hard exudates; CWS, cotton-wool spots; S-location, location indicated by the normal screen coordinate system; and M-location, location indicated by the macula-centered coordinate system.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Comparison Between Diagnoses Made by the Computer System and by the Retinal Specialist, the Grader, and the General Ophthamologist*
Table Graphic Jump LocationTable 2. Statistical Analyses Comparing the Computer System With the Human Experts*
Table Graphic Jump LocationTable 3. Comparisons Between the Computer System and Human Experts and Between the Human Experts (Training Phase)*

References

Centers for Disease Control and Prevention, Prevalence and incidence of diabetes mellitus in United States, 1980-1987. MMWR Morb Mortal Wkly Rep. 1990;39809- 812
Klein  RKlein  BEK Vision disorders in diabetes. Klein  RKlein  BEKeds. Diabetes in America 2nd Bethesda, Md: National Institutes of Health and National Institute of Diabetes and Digestive and Kidney Diseases 1995;293- 339
West  KMErdreich  LJStober  JA A detailed study of risk factors for retinopathy and nephropathy in diabetes. Diabetes. 1980;29501- 508
Link to Article
Newell  SETolbert  BBennett  JParsley  TL The prevalence and risk of diabetic retinopathy among Indians of southwest Oklahoma. J Okla State Med Assoc. 1989;82414- 424
Lee  ETLee  VSKingsley  RM  et al.  Diabetic retinopathy in Oklahoma Indians with NIDDM. Diabetes Care. 1992;151620- 1627
Link to Article
Lee  ETLee  VSLum  MRussell  D Development of proliferative retinopathy in NIDDM: a Follow-up Study of American Indians in Oklahoma. Diabetes. 1992;41359- 367
Link to Article
Leverton  C Grading diabetic retinopathy from a computer screen. J Audiov Media Med. 1996;19176
Lee  SC A real-time high-resolution color imaging system to aid ophthalmologists in diagnosing/grading diabetic retinopathy. Diabetes Abstract Book, 57th Annual Meeting and Scientific Sessions. 46suppl 1 Alexandria, Va Journal of the American DiabetesAssociation1997;334A
Lee  SC Screening for early diabetic retinopathy by a high resolution computer vision system.  Abstracts of the 16th IDF Congress July 20-25, 1997 Helsinki, FinlandA497Diabetologia 1997;40suppl 1I- VIA1- A722
Ryder  REKong  NBates  AS  et al.  Instant electronic imaging systems are superior to Polaroid at detecting sight-threatening diabetic retinopathy. Diabet Med. 1998;15254- 258
Link to Article
Yogesan  KConstable  IJEikelboom  RH  et al.  Tele-ophthalmic screening using digital imaging devices. Aust N Z J Ophthalmol. 1998;26suppl 1S9- S11
Link to Article
Moss  SEKlein  RKessler  SD  et al.  Comparison between ophthalmoscopy and fundus photographs in determining severity of diabetic retinopathy. Ophthalmology. 1985;9262- 67
Link to Article
Schachat  APHyman  LLeske  MC  et al.  Comparison of diabetic retinopathy detection by clinical examinations and photograph gradings: Barbados (West Indies) Eye Study Group. Arch Ophthalmol. 1993;1111064- 1070
Link to Article
Lee  VSKingsley  RMLee  ET  et al.  The diagnosis of diabetic retinopathy. Ophthalmology. 1993;1001504- 1512
Link to Article
George  LDLeverton  CYoung  SLusty  JDunstan  FDOwens  DR Can digitised color 35mm transparencies be used to diagnose diabetic retinopathy?  Abstracts of the 16th IDF Congress July 20-25, 1997 Helsinki, FinlandA496Diabetologia 1997;40suppl 1I- VIA1- A722
Link to Article
Prendergast  JDorsey  CMayes  P Digital retinopathy screening. Diabetes Abstract Book, 57th Annual Meeting and Scientific Sessions46suppl 1 Alexandria, Va Journal of the American Diabetes Association1997;341A
Owens  DRGibbons  RLLewis  PAWall  SAllen  JCMorton  R Screening for diabetic retinopathy by general practitioners: ophthalmoscopy or retinal photography as 35 mm colour transparencies? Diabet Med. 1998;15170- 175
Link to Article
Spencer  TPhillips  RPSharp  PFForrester  J Automated detection and quantification of microaneurysms in fluorescein angiograms. Graefs Arch Clin Exp Ophthalmol. 1992;23036- 41
Link to Article
Baudoin  CEManeschi  FQuentel  G  et al.  Quantitative evaluation of fluorescein angiograms: microaneurysm counts. Diabetes. 1984;33suppl 28- 13
Link to Article
Cree  MJOlson  JAMcHardy  KCSharp  PFForrester  JV A fully automated comparative microaneurysm digital detection system. Eye. 1997;11622- 628
Link to Article
Gardner  GGKeating  DWilliamson  THElliot  AT Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol. 1996;80940- 944
Link to Article
Aleynikov  SMicheli-Tzanakou  E Classification of retinal damage by a neural network based system. J Med Syst. 1998;22129- 136
Link to Article
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