Skip to main content
Top

02-21-2015 | Retinopathy | Review | Article

Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine

Journal: Current Diabetes Reports

Authors: Dawn A. Sim, Pearse A. Keane, Adnan Tufail, Catherine A. Egan, Lloyd Paul Aiello, Paolo S. Silva

Publisher: Springer US

Abstract

There will be an estimated 552 million persons with diabetes globally by the year 2030. Over half of these individuals will develop diabetic retinopathy, representing a nearly insurmountable burden for providing diabetes eye care. Telemedicine programmes have the capability to distribute quality eye care to virtually any location and address the lack of access to ophthalmic services. In most programmes, there is currently a heavy reliance on specially trained retinal image graders, a resource in short supply worldwide. These factors necessitate an image grading automation process to increase the speed of retinal image evaluation while maintaining accuracy and cost effectiveness. Several automatic retinal image analysis systems designed for use in telemedicine have recently become commercially available. Such systems have the potential to substantially improve the manner by which diabetes eye care is delivered by providing automated real-time evaluation to expedite diagnosis and referral if required. Furthermore, integration with electronic medical records may allow a more accurate prognostication for individual patients and may provide predictive modelling of medical risk factors based on broad population data.
Literature
1.
Jaeger E. Beitr zur Pathol des Auges. Wien p. 1856;33 Fig 12.
2.
Kempen JH, O'Colmain BJ, Leske MC, Haffner SM, Klein R, Moss SE, et al. The prevalence of diabetic retinopathy among adults in the United States. Arch Ophthalmol. 2004;122:552–63.CrossRefPubMed
3.
Roy MS, Klein R, O'Colmain BJ, Klein BE, Moss SE, Kempen JH. The prevalence of diabetic retinopathy among adult type 1 diabetic persons in the United States. Arch Ophthalmol. 2004;122:546–51.CrossRefPubMed
4.
Klein R, Klein BE, Moss SE, Davis MD, DeMets DL. The Wisconsin epidemiologic study of diabetic retinopathy. III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. Arch Ophthalmol. 1984;102:527–32.CrossRefPubMed
5.
Klein R, Klein BE, Moss SE, Davis MD, DeMets DL. The Wisconsin epidemiologic study of diabetic retinopathy. II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch Ophthalmol. 1984;102:520–6.CrossRefPubMed
6.
Banting FG, Best CH, Collip JB, Campbell WR, Fletcher AA. Pancreatic extracts in the treatment of diabetes mellitus. Can Med Assoc J. 1922;12:141–6.PubMedCentralPubMed
7.
Diabetic Retinopathy Clinical Research N, Writing C, Aiello LP, Beck RW, Bressler NM, Browning DJ, et al. Rationale for the diabetic retinopathy clinical research network treatment protocol for center-involved diabetic macular edema. Ophthalmology. 2011;118:e5–14.CrossRef
8.
Brown DM, Nguyen QD, Marcus DM, Boyer DS, Patel S, Feiner L, et al. Long-term outcomes of ranibizumab therapy for diabetic macular edema: the 36-month results from two phase III trials: RISE and RIDE. Ophthalmology. 2013;120:2013–22.CrossRefPubMed
9.
Korobelnik JF, Do DV, Schmidt-Erfurth U, Boyer DS, Holz FG, Heier JS, Midena E, Kaiser PK, Terasaki H, Marcus DM, et al. Intravitreal aflibercept for diabetic macular edema. Ophthalmology 2014;121:2247–54.
10.
Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology. 1991;98:766–785.
11.
Hartnett ME, Key IJ, Loyacano NM, Horswell RL, Desalvo KB. Perceived barriers to diabetic eye care: qualitative study of patients and physicians. Arch Ophthalmol. 2005;123:387–91.CrossRefPubMed
12.
Liew G, Michaelides M, Bunce C. A comparison of the causes of blindness certifications in England and Wales in working age adults (16–64 years), 1999–2000 with 2009–2010. BMJ Open. 2014;4:e004015.CrossRefPubMedCentralPubMed
13.
Whited JD, Datta SK, Aiello LM, Aiello LP, Cavallerano JD, Conlin PR, et al. A modeled economic analysis of a digital tele-ophthalmology system as used by three federal health care agencies for detecting proliferative diabetic retinopathy. Telemed J E Health. 2005;11:641–51.CrossRefPubMed
14.
Wilson C, Horton M, Cavallerano J, Aiello LM. Addition of primary care-based retinal imaging technology to an existing eye care professional referral program increased the rate of surveillance and treatment of diabetic retinopathy. Diabetes Care. 2005;28:318–22.CrossRefPubMed
15.
Silva PS, Cavallerano JD, Aiello LM, Aiello LP. Telemedicine and diabetic retinopathy: moving beyond retinal screening. Arch Ophthalmol. 2011;129:236–42.CrossRefPubMed
16.
Program, N.D.E.S. 2010–2011 Annual report. http://​diabeticeye.​screening.​nhs.​uk/​reports. Accessed 16 July 2014.
17.
Li B, Li HK. Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends. Curr Diab Rep. 2013;13:453–9.CrossRefPubMed
18.
Mookiah MR, Acharya UR, Chua CK, Lim CM, Ng EY, Laude A. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med. 2013;43:2136–55.CrossRefPubMed
19.
Teng T, Lefley M, Claremont D. Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med Biol Eng Comput. 2002;40:2–13.CrossRefPubMed
20.
Fleming AD, Philip S, Goatman KA, Prescott GJ, Sharp PF, Olson JA. The evidence for automated grading in diabetic retinopathy screening. Curr Diabetes Rev. 2011;7:246–52.CrossRefPubMed
21.
Grading diabetic retinopathy from stereoscopic color fundus photographs—an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology 1991;98:786–806.
22.
Silva PS, Cavallerano JD, Aiello LM. Ocular telehealth initiatives in diabetic retinopathy. Curr Diab Rep. 2009;9:265–71.CrossRefPubMed
23.
Li HK, Horton M, Bursell SE, Cavallerano J, Zimmer-Galler I, Tennant M, et al. Telehealth practice recommendations for diabetic retinopathy, second edition. Telemed J E Health. 2011;17:814–37.CrossRefPubMed
24.
Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, et al. Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res. 2006;25:99–127.CrossRefPubMed
25.
Li HK, Esquivel A, Hubbard LD, Florez-Arango JF, Danis RP, Krupinski EA. Mosaics versus early treatment diabetic retinopathy seven standard fields for evaluation of diabetic retinopathy severity. Retina. 2011;31:1553–63.CrossRefPubMed
26.
Silva PS, Cavallerano JD, Sun JK, Soliman AZ, Aiello LM, Aiello LP. Peripheral lesions identified by mydriatic ultrawide field imaging: distribution and potential impact on diabetic retinopathy severity. Ophthalmology. 2013;120:2587–95.CrossRefPubMed
27.
Hubbard LD, Sun W, Cleary PA, Danis RP, Hainsworth DP, Peng Q, et al. Comparison of digital and film grading of diabetic retinopathy severity in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Arch Ophthalmol. 2011;129:718–26.CrossRefPubMed
28.
Gangaputra S, Almukhtar T, Glassman AR, Aiello LP, Bressler N, Bressler SB, et al. Comparison of film and digital fundus photographs in eyes of individuals with diabetes mellitus. Invest Ophthalmol Vis Sci. 2011;52:6168–73.CrossRefPubMedCentralPubMed
29.
Silva PS, Cavallerano JD, Tolls D, Omar A, Thakore K, Patel B, et al. Potential efficiency benefits of nonmydriatic ultrawide field retinal imaging in an ocular telehealth diabetic retinopathy program. Diabetes Care. 2014;37:50–5.CrossRefPubMed
30.
Ahmed J, Ward TP, Bursell SE, Aiello LM, Cavallerano JD, Vigersky RA. The sensitivity and specificity of nonmydriatic digital stereoscopic retinal imaging in detecting diabetic retinopathy. Diabetes Care. 2006;29:2205–9.CrossRefPubMed
31.
Scanlon PH, Foy C, Malhotra R, Aldington SJ. The influence of age, duration of diabetes, cataract, and pupil size on image quality in digital photographic retinal screening. Diabetes Care. 2005;28:2448–53.CrossRefPubMed
32.
Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest Ophthalmol Vis Sci. 2006;47:1120–5.CrossRefPubMed
33.
Maker MP, Noble J, Silva PS, Cavallerano JD, Murtha TJ, Sun JK, et al. Automated Retinal Imaging System (ARIS) compared with ETDRS protocol color stereoscopic retinal photography to assess level of diabetic retinopathy. Diabetes Technol Ther. 2012;14:515–22.CrossRefPubMed
34.
Smolek MKN N, Jaramillo A, Diamond JG, Batlle OR. Image quality and disease screening performance comparison of clinic-based and telemedicine-based retinal cameras. ARVO/ISIE Imaging Conference 2012. 2012 Poster Board Number: P55.
35.
Davis H, Russell S, Barriga E, Abramoff M, Soliz P. Vision-based, real-time retinal image quality assessment. In Computer-based medical systems, 2009. CBMS 2009. 22nd IEEE International Symposium on 2009. 1–6.
36.
Spencer T, Phillips RP, Sharp PF, Forrester JV. Automated detection and quantification of microaneurysms in fluorescein angiograms. Graefes Arch Clin Exp Ophthalmol. 1992;230:36–41.CrossRefPubMed
37.
Diabetic retinopathy study. Report Number 6. Design, methods, and baseline results. Report Number 7. A modification of the Airlie House classification of diabetic retinopathy. Prepared by the Diabetic Retinopathy. Invest Ophthalmol Vis Sci. 1981;21:1–226.
38.
Antal B, Hajdu A. An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng. 2012;59:1720–6.CrossRefPubMed
39.
Fleming AD, Goatman KA, Philip S, Williams GJ, Prescott GJ, Scotland GS, et al. The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. Br J Ophthalmol. 2010;94:706–11.CrossRefPubMed
40.
Quellec G, Lamard M, Abramoff MD, Decenciere E, Lay B, Erginay A, et al. A multiple-instance learning framework for diabetic retinopathy screening. Med Image Anal. 2012;16:1228–40.CrossRefPubMed
41.
Quellec G, Lamard M, Cazuguel G, Bekri L, Daccache W, Roux C, et al. Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs. Invest Ophthalmol Vis Sci. 2011;52:8342–8.CrossRefPubMed
42.
http://​www.​medalytix.​com Accessed 15th July 2014.
43.
Philip S, Fleming AD, Goatman KA, Fonseca S, McNamee P, Scotland GS, et al. The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme. Br J Ophthalmol. 2007;91:1512–7.CrossRefPubMedCentralPubMed
44.
Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging. 2006;25:1223–32.CrossRefPubMed
45.•
Fleming AD, Goatman KA, Philip S, Prescott GJ, Sharp PF, Olson JA. Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts. Br J Ophthalmol. 2010;94:1606–10. Key paper for the iGrading Program.CrossRefPubMed
46.
Scotland GS, McNamee P, Philip S, Fleming AD, Goatman KA, Prescott GJ, et al. Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland. Br J Ophthalmol. 2007;91:1518–23.CrossRefPubMedCentralPubMed
47.
Scotland GS, McNamee P, Fleming AD, Goatman KA, Philip S, Prescott GJ, et al. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy. Br J Ophthalmol. 2010;94:712–9.CrossRefPubMed
48.
Goatman K, Charnley A, Webster L, Nussey S. Assessment of automated disease detection in diabetic retinopathy screening using two-field photography. PLoS One. 2011;6:e27524.CrossRefPubMedCentralPubMed
49.
50.
Karnowski TP, Giancardo L, Li Y, Tobin KW, Chaum E. Retina image analysis and ocular telehealth: The Oak Ridge National Laboratory–Hamilton Eye Institute case study. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:7140–3.PubMed
51.
Garg S, Jani PD, Kshirsagar AV, King B, Chaum E. Telemedicine and retinal imaging for improving diabetic retinopathy evaluation. Arch Intern Med. 2012;172:1677–8.CrossRefPubMed
52.
Tobin KW, Chaum E, Govindasamy VP, Karnowski TP. Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging. 2007;26:1729–39.CrossRefPubMed
53.•
Chaum E, Karnowski TP, Govindasamy VP, Abdelrahman M, Tobin KW. Automated diagnosis of retinopathy by content-based image retrieval. Retina. 2008;28:1463–77. Key paper for the TRIAD program.CrossRefPubMed
54.
Tobin KW, Abramoff MD, Chaum E, Giancardo L, Govindasamy V, Karnowski TP, et al. Using a patient image archive to diagnose retinopathy. Conf Proc IEEE Eng Med Biol Soc. 2008;2008:5441–4.PubMed
55.
Tobin KW, Abdelrahman M, Chaum E, Govindasamy V, Karnowski TP. A probabilistic framework for content-based diagnosis of retinal disease. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:6744–7.PubMed
56.
http://​www.​eyediagnosis.​net Accessed 15th July 2014.
57.
Niemeijer M, Abramoff MD, van Ginneken B. Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Anal. 2006;10:888–98.CrossRefPubMed
58.
Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MS, Abramoff MD. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging. 2005;24:584–92.CrossRefPubMed
59.
Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C. Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging. 2008;27:1230–41.CrossRefPubMedCentralPubMed
60.
Niemeijer M, van Ginneken B, Russell SR, Suttorp-Schulten MS, Abramoff MD. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Invest Ophthalmol Vis Sci. 2007;48:2260–7.CrossRefPubMedCentralPubMed
61.
Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abramoff MD. Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans Med Imaging. 2013;32:364–75.CrossRefPubMed
62.
Abramoff MD, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russell SR, van Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care. 2008;31:193–8.CrossRefPubMed
63.
Abramoff MD, Reinhardt JM, Russell SR, Folk JC, Mahajan VB, Niemeijer M, et al. Automated early detection of diabetic retinopathy. Ophthalmology. 2010;117:1147–54.CrossRefPubMedCentralPubMed
64.
Niemeijer M, Abramoff MD, van Ginneken B. Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Trans Med Imaging. 2009;28:775–85.CrossRefPubMed
65.•
Abramoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013;131:351–7. Key paper for the IDx Program.CrossRefPubMed
66.
In http://​www.​retmarker.​com/​ Accessed 15th July 2014.
67.
Pires Dias JM, Oliveira CM, da Silva Cruz LA. Retinal image quality assessment using generic image quality indicators. Information Fusion. 2014;19:73–90.CrossRef
68.
Nunes S, Pires I, Rosa A, Duarte L, Bernardes R, Cunha-Vaz J. Microaneurysm turnover is a biomarker for diabetic retinopathy progression to clinically significant macular edema: findings for type 2 diabetics with nonproliferative retinopathy. Ophthalmologica. 2009;223:292–7.CrossRefPubMed
69.•
Bernardes R, Nunes S, Pereira I, Torrent T, Rosa A, Coelho D, et al. Computer-assisted microaneurysm turnover in the early stages of diabetic retinopathy. Ophthalmologica. 2009;223:284–91. Key paper for the Retmarker program.CrossRefPubMed
70.
Nunes S, Ribeiro L, Lobo C, Cunha-Vaz J. Three different phenotypes of mild nonproliferative diabetic retinopathy with different risks for development of clinically significant macular edema. Invest Ophthalmol Vis Sci. 2013;54:4595–604.CrossRefPubMed
71.
Haritoglou C, Kernt M, Neubauer A, Gerss J, Oliveira CM, Kampik A, et al. Microaneurysm formation rate as a predictive marker for progression to clinically significant macular edema in nonproliferative diabetic retinopathy. Retina. 2014;34:157–64.CrossRefPubMed
72.
Ribeiro ML, Nunes SG, Cunha-Vaz JG. Microaneurysm turnover at the macula predicts risk of development of clinically significant macular edema in persons with mild nonproliferative diabetic retinopathy. Diabetes Care. 2013;36:1254–9.CrossRefPubMedCentralPubMed
73.
http://​retinalyze.​net. Accessed 10th August 2014.
74.
Larsen N, Godt J, Grunkin M, Lund-Andersen H, Larsen M. Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthalmol Vis Sci. 2003;44:767–71.CrossRefPubMed
75.
Hansen AB, Hartvig NV, Jensen MS, Borch-Johnsen K, Lund-Andersen H, Larsen M. Diabetic retinopathy screening using digital non-mydriatic fundus photography and automated image analysis. Acta Ophthalmol Scand. 2004;82:666–72.CrossRefPubMed
76.
Larsen M, Godt J, Larsen N, Lund-Andersen H, Sjolie AK, Agardh E, et al. Automated detection of fundus photographic red lesions in diabetic retinopathy. Invest Ophthalmol Vis Sci. 2003;44:761–6.CrossRefPubMed
77.•
Bouhaimed M, Gibbins R, Owens D. Automated detection of diabetic retinopathy: results of a screening study. Diabetes Technol Ther. 2008;10:142–8. Key paper for the retinalyze program.CrossRefPubMed
78.
80.
Mitry D, Peto T, Hayat S, Morgan JE, Khaw KT, Foster PJ. Crowdsourcing as a novel technique for retinal fundus photography classification: analysis of images in the EPIC Norfolk cohort on behalf of the UK Biobank Eye and Vision Consortium. PLoS One. 2013;8:e71154.CrossRefPubMedCentralPubMed
81.
82.••
Trucco E, Ruggeri A, Karnowski T, Giancardo L, Chaum E, Hubschman JP, et al. Validating retinal fundus image analysis algorithms: issues and a proposal. Invest Ophthalmol Vis Sci. 2013;54:3546–59. Provides a framework and reference standard for the evaluation of automated retinal image anaylsis systems.CrossRefPubMed

Be confident that your patient care is up to date

Medicine Matters is being incorporated into Springer Medicine, our new medical education platform. 

Alongside the news coverage and expert commentary you have come to expect from Medicine Matters diabetes, Springer Medicine's complimentary membership also provides access to articles from renowned journals and a broad range of Continuing Medical Education programs. Create your free account »