Tackling teleretinal screening for diabetic retinopathy

Modern Retina Digital Edition, Modern Retina Fall 2021, Volume 1, Issue 2

Options facilitate increased durability for patients with retinal degeneration.

Diabetic retinopathy (DR) is the leading cause of permanent blindness in working-age adults, and the number of patients with diabetes worldwide is expected to reach 693 million by 2045.1 Consequently, it is necessary to find a more effective means of screening patients for diabetic retinopathy.2

A viable testing option is teleretinal screening for diabetic retinopathy (TSDR), a technology that has been used successfully in a variety of cultures and demographic settings, has been well reported in recent years,3-9 and whose cost effectiveness has also been confirmed.10-14

Typically, referable diabetic retinopathy (RDR) is defined as diabetic retinopathy worse than mild nonproliferative diabetic retinopathy and/or macular edema, based on the international classification of diabetic retinopathy.

In longstanding (10-20 years) TSDR programs, the incidence of sight-threatening diabetic retinopathy decreases over time, suggesting the program’s effectiveness and the possible need to refine the indications for screening in those programs.15-16

Artificial intelligence

Recent publications have discussed the use of artificial intelligence (AI) as well as different cameras, including tabletop and handheld devices, to further TSDR programs.17-26

Multiple deep learning image assessment software have been developed.26-32 To date, two AI-based autonomous cameras have been approved by the FDA (IDx-DR, Digital Diagnostics; Eyenuk), and several other automated AI-based algorithms with varying degrees of sensitivity and specificity are available worldwide.33

Sensitivity is a measure of how well a test can identify true positives and specificity, a measure of how well it can identify true negatives. In both diagnostic and screening tests, there is usually a tradeoff between sensitivity and specificity, such that higher sensitivities will mean lower specificities and vice versa. Sensitivity (true positive rate) is determined by dividing the number of true positives by the total number of positives. Specificity (true negative rate) is the number of true negatives divided by the total number of negatives. In TSDR programs, a high specificity rate is more important than a high sensitivity rate to ensure that patients with the disease are seen by a specialist.

Although a high sensitivity rate could ideally keep individuals without disease out of the specialist’s office, it is better to see “false positives” (due to lower sensitivity) than not see “false negatives” (due to low specificity). TSDR programs with high specificity will do a better job of not missing patients with disease, thus meeting its main objective of identifying patients in need of a DR specialist.

Three imaging options

Three different fundus images have been used in TSDR programs. The gold standard is the Early Treatment Diabetic Retinopathy Study (ETDRS) 7 standard field (7SF) image. However, the burden of multiple image capture makes 7SF photographs unrealistic for a TSDR program. The most common image capture technique is less than 7SF, which usually involves an image of the posterior pole that includes the disc, macula, and vascular arcades. Alternatively, ultra-widefield images, which capture over 200 degrees of retina, have also been used in some DR screening programs.34 These techniques were found to be essentially similar in effectiveness, although the less than 7SF may have a higher number of ungradable images.35

Autonomous AI software is gradually being integrated into TSDR programs, but the current standard is point-of-care image capture with a fundus camera in a primary care setting, followed by a review of the image by an eye care provider via a web-based platform. One advantage of this method over the FDA-approved autonomous interpretation algorithms is that it allows screening not only for diabetic eye disease but also for such other pathologies as cataracts (hazy view), glaucoma (increased optic disc cupping), macular holes, epiretinal membranes, and macular degenerations, making it possible for patients with sight-threatening disease to triaged and, depending on diagnosis, contacted for a timely examination.

Reading centers vary depending upon the health care system; some readers are located in the same community as the camera, and others are centralized in a distant center. A study confirmed some utility in centralizing the reading center.36 But there are benefits to keeping the reading center in the same community as the cameras, including increased accountability among patients, primary care physicians, reading centers, and ophthalmologists. A systems-based approach allows all stakeholders to give input when determining processes that improve adherence rates. One study suggests that local collaboration may increase adherence rates.37 Local relationships and referral patterns enable better communication among stakeholders, and community pride may promote cooperation in reducing risk of vision loss in these patients.

At present, AI algorithms are FDA-approved only for the detection of diabetic eye disease (DED) and/or RDR. Consequently, other pathologies will be missed by screening programs that employ only the AI component. This limitation may become more important as teleretinal imaging expands to screen for other disease states.

Missing follow-ups

A criticism of TSDR programs is lack of adherence to the recommended follow-up exams among patients with RDR, even though adherence rates range widely, from 9.5% to 81.9% depending on the study.37-38 Adherence rates for these follow-up exams are determined by dividing the ratio of the number of patients seen by a specialist by the number of patients diagnosed with RDR. In one study, automated retinal image analysis (performed in a primary care setting and allowing for immediate interpretive feedback to the patient) improved adherence by more than 36 percentage points, from 18.7% to 55.4%.22

Consequently, AI cameras may not only provide instantaneous diagnostic information to patients, but also increase adherence rates among patients with RDR. Conversely, barriers to full utilization exist in some programs that make implementation difficult.39 The incidence of RDR increases with failure to attend screening appointments: 5-9 consecutive misses increase RDR likelihood from 4% to 15%, and 10 or more consecutive misses increase RDR likelihood to 20%.40

Reports confirm TSDR is able to detect macular edema, with one report confirming the presence of lipid, which increases the chances of macular edema. However, sensitivity and specificity in diagnosing macular edema varies widely among reports.14, 41-42

Initially, these TSDR programs were focused on underserved communities, be they uninsured, rural, or urban public health systems. More recently, insured population groups have been studied, likewise with promising results. TSDR is effective for all population subgroups. As one would expect, regions and/or communities with lower access to health care have higher DR rates and, consequently, potentially would benefit more from the deployment of TSDR.14

TSDR is likely to become the standard strategy for screening diabetic patients for DR in most clinical settings around the world. Ophthalmologists engaged in the fight to prevent blindness from DR will be well-served by understanding the different aspects of TSDR and their communities by being involved in its deployment.

--

Jose Agustin Martinez, MD
P: (512) 451-0103
Martinez reports no financial disclosures related to this content.

--

References
1. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271-281. doi: 10.1016/j.diabres.2018.02.023
2. Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review.Clin Exp Ophthalmol. 2016;44(4):260-277. doi: 10.1111/ceo.12696
3. Egunsola O, Dowsett LE, Diaz R, Brent MH, Rac V, Clement FM. Diabetic retinopathy screening: a systematic review of qualitative literature. Can J Diabetes. 2021;S1499-2671(21)00032-0. doi: 10.1016/j.jcjd.2021.01.014
4. Cao J, Felfeli T, Merritt R, Brent MH. Sociodemographics associated with risk of diabetic retinopathy detected by tele-ophthalmology: 5-year results of the Toronto tele-retinal screening program. Can J Diabetes. 2021:S1499-2671 (21)00132-5. doi: 10.1016/j.jcjd.2021.05.001
5. Huemer J, Wagner SK, Sim DA. The evolution of diabetic retinopathy screening programmes: a chronology of retinal photography from 35 mm slides to artificial intelligence. Clin Ophthalmol. 2020;14:2021-2035. doi: 10.2147/OPTH.S261629
6. Zapata MA, Arcos G, Fonollosa A, Abraldes M, Abraldes M, Oleñik A, et al. Telemedicine for a general screening of retinal disease using nonmydriatic fundus cameras in optometry centers: three-year results. Telemed J E Health. 2017;23(1):30-36. doi: 10.1089/tmj.2016.0020
7. Sharma A. Emerging simplified retinal imaging. 2017;60:56-62. doi:10.1159/000459690
8. Rosses AP, Ben ÂJ, Furtado de Souza C, Skortika A, Lutz de Araújo A, de Carvalho G, et al. Diagnostic performance of retinal digital photography for diabetic retinopathy screening in primary care. Fam Pract. 2017;34(5):546–551. doi:10.1093/fampra/cmx020
9. Porta M, Boscia F, Lanzetta P, Mannucci E, Menchini U, Simonelli F. Systematic screening of retinopathy in diabetes (read project): an Italian implementation campaign. Eur J Ophthalmol. 2017;27(2):179-184. doi: 10.5301/ejo.5000912
10. Hristova E, Koseva D, Ziatarova Z, Dakota K. Diabetic retinopathy screening and registration in Europe–narrative review.Healthcare (Basel). 2021;9(6):745. doi:10.3390/healthcare9060745
11. Ahern S, Riordan F, Murphy A, Browne J, Kearney PM, Smith SM, et al. A micro costing analysis of the development of a primary care intervention to improve the uptake of diabetic retinopathy screening. Implement Sci.2021;16(1):17. doi:10.1186/s13012-021-01085-4
12. Nguyen, HV, Tan GS, Tapp RJ, Mital S, Ting DS, Wong HT et al. Cost-effectiveness of a national telemedicine diabetic retinopathy screening program in Singapore. Ophthalmology. 2016;123(12):2571-2580. doi:10.1016/j.ophtha.2016.08.021
13. Wolf RM, Channa R, Abramoff MD, Lehmann HP. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol. 2020;138(10):1063-1069. doi: 10.1001/jamaophthalmol.2020.319014
14. Ullah W, Pathan SK, Panchal A, Anandan S, Saleem K, Sattar Y, et al. Cost-effectiveness and diagnostic accuracy of telemedicine in macular disease and diabetic retinopathy: a systematic review and meta-analysis. Medicine (Baltimore). 2020;99(25):e20306. doi:10.1097/MD.0000000000020306
15. Cheyne CP, Burgess PI, Broadbent DM, García-Fiñana M, Stratton IM, Criddle T, et al. Incidence of sight-threatening diabetic retinopathy in an established urban screening programme: an 11-year cohort study. Diabet Med. 2021;38(9):e14583. doi:10.1111/dme.14583
16. Chamard C, Daien V, Erginay A, Gautier JF, Villain M, Tadayoni R, et al. Ten-year incidence and assessment of safe screening intervals for diabetic retinopathy: the OPHDIAT study. Br J Ophthalmol. 2021;105(3):432-439. doi:10.1136/bjophthalmol-2020-316030
17. Shah A, Clarida W, Amelon R, Hernáez-Ortega M, Navea A, Morales-Olivas J, et al. Validation of automated screening for referable diabetic retinopathy with an autonomous diagnostic artificial intelligence system in a spanish population. J Diabetes Sci Technol. 2021;15(3): 655-663. doi:10.1177/1932296820906212
18. Quinn N, Brazionis L, Zhu B, Ryan C, D’Aloisio R,Tang HL, et al. Facilitating diabetic retinopathy screening using automated retinal image analysis in underresourced settings. Diabet Med. 2021;38(9):e14582. doi:10.1111/dme.14582
19. Peris-Martinez C, Shaha A, Clarida W, Amelon R, Hernáez-Ortega MC, Navea A, et al. Use in clinical practice of an automated screening method of diabetic retinopathy that can be derived using a diagnostic artificial intelligence system. Arch Soc Esp Oftalmol (Engl Ed). 2021;96(3):117-126. doi:10.1016/j.oftal.2020.08.007
20. Ming S, Xie K, Lei X, Yang Y, Zhao Z, Li S, et al. Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study. Int Ophthalmol. 2021;41(4):1291-1299. doi:10.1007/s10792-020-01685-x
21. Malerbi FK, Andrade RE, Morales PH, Stuchi JA, Lencione D, de Paulo JV, et al. Diabetic retinopathy screening using artificial intelligence and handheld smartphone-based retinal camera. J Diabetes Sci Technol. 2021;1932296820985567. doi:10.1177/1932296820985567
22. Liu J, Gibson E, Ramchal S, Shankar V, Piggott K, Sychev Y, et al. Diabetic retinopathy screening with automated retinal image analysis in a primary care setting improves adherence to ophthalmic care. Ophthalmol Retina. 2021;5(1):71-77. doi: 10.1016/j.oret.2020.06.016
23. Lim WS, Grimaldi G, Nicholson L, Basheer K, Rajendram R. Widefield imaging with Clarus fundus camera vs slit lamp fundus examination in assessing patients referred from the National Health Service diabetic retinopathy screening programme. Eye (Lond). 2021;35(1):299-306. doi:10.1038/s41433-020-01218-x
24. Kubin AM, Wirkkala J, Keskitalo A, Ohtonen P, Hautala N. Handheld fundus camera performance, image quality and outcomes of diabetic retinopathy grading in a pilot screening study. Acta Ophthalmol. 2021;10.1111/aos.14850. doi:10.1111/aos.14850
25. Queiroz MS, de Carvalho JX, Fereira Bortoto S, de Matos MR, Dias Cavalcante CdG, Silva Andrade EA, et al. Diabetic retinopathy screening in urban primary care setting with a handheld smartphone-based retinal camera. Acta Diabetol. 2020;57(12):1493-1499. doi:10.1007/s00592-020-01585-7
26. Walton OB IV, Garoon RB, Weng CY, Gross J, Young AK, Camero KA, et al. Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol. 2016;134(2):204-209. doi:10.1001/jamaophthalmol.2015.5083
27. Hsieh YT, Chuang LM, Jiang YD, Chan TJ, Yang CM, Yang CH, et al. Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening. J Formos Med Assoc. 2021;120(1, pt 1):165-171. doi:10.1016/j.jfma.2020.03.024
28. Zhang W, Nicholas P, Schuman SG, Allingham MJ, Faridi A, Suthar T, et al. Screening for diabetic retinopathy using a portable, noncontact, nonmydriatic handheld retinal camera. J Diabetes Sci Technol. 2017;11(1):128-134. doi:10.1177/1932296816658902.
29. Heydon P, Egan C, Bolter L, Chambers R, Anderson J, Aldington S, Stratton IM, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. 2021;105(5):723-728. doi: 10.1136/bjophthalmol-2020-316594
30. Cuadros J. The real-world impact of artificial intelligence on diabetic retinopathy screening in primary care. J Diabetes Sci Technol, 2021;15(3):664-665. doi:10.1177/1932296820914287
31. Arenas-Cavalli JT, Abarca I, Rojas-Contreras M, Bernuy F, Donoso R. Clinical validation of an artificial intelligence-based diabetic retinopathy screening tool for a national health system. Eye (Lond). 2021;35(10):2910. doi:10.1038/s41433-020-01366-0
32. Vaghefi E, Yang S, Xie L, Hill S, Schmiedel O, Murphy R, et al. THEIA™ development, and testing of artificial intelligence–based primary triage of diabetic retinopathy screening images in New Zealand.Diabet Med. 2021;38(4):e14386. doi:10.1111/dme.14386
33. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, et al. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care. 2021 ;44(5):1168-1175. doi:10.2337/dc20-1877
34. Kato A, Fujishima K, Inoue N, Takase N, Suzuki N, Kuwayama S, et al. Remote screening of diabetic retinopathy using ultra-widefield retinal imaging. Diabetes Res Clin Pract, 2021;177:108902. doi:10.1016/j.diabres.2021.108902
35. Kárason KT, Vo D, Grauslund J, Lundberg Rasmussen M, et al., Comparison of different methods of retinal imaging for the screening of diabetic retinopathy: a systematic review. Acta Ophthalmol. 2021;10.1111/aos.14767. doi:10.1111/aos.14767
36. Melles RB, et al, Conell C, Siegner SW, Tarasewicz D. Diabetic retinopathy screening using a virtual reading center. Acta Diabetol. 2020;57(2):183-188. doi:10.1007/s00592-019-01392-9
37. Martinez JA, Parikh PD, Wong RW, Harper CA, Dooner JW, Levitan M, et al. Telemedicine for diabetic retinopathy screening in an urban, insured population using fundus cameras in a primary care office setting. Ophthalmic Surg Lasers Imaging Retina. 2019;50(11):e274-e277. doi:10.3928/23258160-20191031-14
38. Benjamin JE, Sun J, Cohen D , Matz J, Barbera A, Henderer J, et al. A 15 month experience with a primary care-based telemedicine screening program for diabetic retinopathy. BMC Ophthalmol. 2021; 4;21(1):70. doi:10.1186/s12886-021-01828-3
39. Bastos de Carvalho A, Ware SL, Belcher T, Mehmeti F, Higgins EB, Sprang R, et al. Evaluation of multi-level barriers and facilitators in a large diabetic retinopathy screening program in federally qualified health centers: a qualitative study. Implement Sci Commun. 2021;2(1):54. doi:10.1186/s43058-021-00157-2
40. Virk R, Binns AM, Chambers R, Anderson J. How is the risk of being diagnosed with referable diabetic retinopathy affected by failure to attend diabetes eye screening appointments? Eye (Lond). 2021;35(2):477-483. doi:10.1038/s41433-020-0877-1
41. Litvin TV, Weissenberg CR, Daskivich LP, Zhou Q, Bresnick GH, Cuadros JA. Improving accuracy of grading and referral of diabetic macular edema using location and extent of hard exudates in retinal photography. J Diabetes Sci Technol. 2016;10(2):262-70. doi: 10.1177/1932296815617281
42. Litvin TV, Bresnick GH, Cuadros JA, Selvin S, Kanai K, Ozawa GY. A revised approach for the detection of sight-threatening diabetic macular edema. JAMA Ophthalmol. 2017; 135(1):62-68. doi:10.1001/jamaophthalmol.2016.4772