Advertisement

Current Ophthalmology Reports

, Volume 6, Issue 1, pp 36–45 | Cite as

Retinal Telemedicine

  • Ru-ik Chee
  • Dana Darwish
  • Álvaro Fernández-Vega
  • Samir N. Patel
  • Karyn Jonas
  • Susan Ostmo
  • J. Peter Campbell
  • Michael F. Chiang
  • R.V. Paul ChanEmail author
Retina (J Fortun, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Retina

Abstract

Purpose of Review

This study aims for an update and overview of the literature on current telemedicine applications in retina.

Recent Findings

The application of telemedicine to the field of Ophthalmology and Retina has been growing with advancing technologies in ophthalmic imaging. Retinal telemedicine has been most commonly applied to diabetic retinopathy and retinopathy of prematurity in adult and pediatric patients, respectively. Telemedicine has the potential to alleviate the growing demand for clinical evaluation of retinal diseases. Subsequently, automated image analysis and deep learning systems may facilitate efficient processing of large, increasing numbers of images generated in telemedicine systems. Telemedicine may additionally improve access to education and standardized training through tele-education systems.

Summary

Telemedicine has the potential to be utilized as a useful adjunct but not a complete replacement for physical clinical examinations. Retinal telemedicine programs should be carefully and appropriately integrated into current clinical systems.

Keywords

Retina Diabetic retinopathy Retinopathy of prematurity Telemedicine Image analysis Deep learning 

Notes

Acknowledgements

Unrestricted departmental grant from Research to Prevent Blindness (RVPC, RC, KEJ, MRC, PC, SO, DD); National Institutes of Health R01 EY019474, Bethesda, Maryland (RVPC, MFC, JPC, SO); National Science Foundation SCH-1622679, Arlington, Virginia (RVPC, MFC, JPC, SO); National Institutes of Health P30 EY001792 Core Grant for Vision Research (RVPC, RC, KEJ, DD).

Compliance with Ethical Standards

Conflict of Interest

Ru-ik Chee, Dana Darwish, Álvaro Fernández-Vega, Samir Patel, Karyn Jonas, Susan Ostmo, Peter Campbell, and R.V. Paul Chan declare no conflict of interest.

Michael Chiang reports grants from National Institutes of Health, the National Science Foundation, unrestricted department funding from Research to Prevent Blindness, personal fees as a consultant for Novartis (Steering Committee member, RAINBOW study), and is an unpaid member of the scientific advisory board for Clarity Medical Systems.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Rathi S, Tsui E, Mehta N, Zahid S, Schuman JS. The current state of teleophthalmology in the United States. Ophthalmology. 2017;124(12):1729–34.  https://doi.org/10.1016/j.ophtha.2017.05.026.CrossRefPubMedGoogle Scholar
  2. 2.
    Lamirel C, Bruce BB, Wright DW, Delaney KP, Newman NJ, Biousse V. Quality of nonmydriatic digital fundus photography obtained by nurse practitioners in the emergency department: the FOTO-ED study. Ophthalmology. 2012;119(3):617–24.  https://doi.org/10.1016/j.ophtha.2011.09.013.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Rothschild MI, Russ R, Brennan KA, Williams CJ, Berrones D, Patel B, et al. The economic model of retinopathy of prematurity (EcROP) screening and treatment: Mexico and the United States. Am J Ophthalmol. 2016;168:110–21.  https://doi.org/10.1016/j.ajo.2016.04.014.CrossRefPubMedGoogle Scholar
  4. 4.
    Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14.  https://doi.org/10.1016/j.diabres.2009.10.007.CrossRefPubMedGoogle Scholar
  5. 5.
    Bresnick GH, Mukamel DB, Dickinson JC, Cole DR. A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Ophthalmology. 2000;107(1):19–24.  https://doi.org/10.1016/S0161-6420(99)00010-X.CrossRefPubMedGoogle Scholar
  6. 6.
    Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology 1991;98:766–785.Google Scholar
  7. 7.
    Zoega GM, Gunnarsdóttir T, Björnsdóttir S, et al. Screening compliance and visual outcome in diabetes. Acta Ophthalmol Scand. 2005;83(6):687–90.  https://doi.org/10.1111/j.1600-0420.2005.00541.x.CrossRefPubMedGoogle Scholar
  8. 8.
    Lee PP, Feldman ZW, Ostermann J, Brown DS, Sloan FA. Longitudinal rates of annual eye examinations of persons with diabetes and chronic eye diseases. Ophthalmology. 2003;110(10):1952–9.  https://doi.org/10.1016/S0161-6420(03)00817-0.CrossRefPubMedGoogle Scholar
  9. 9.
    Zimmer-Galler IE, Kimura AE, Gupta S. Diabetic retinopathy screening and the use of telemedicine. Curr Opin Ophthalmol. 2015;26(3):167–72.  https://doi.org/10.1097/ICU.0000000000000142.CrossRefPubMedGoogle Scholar
  10. 10.
    Chasan JE, Delaune B, Maa AY, Lynch MG. Effect of a teleretinal screening program on eye care use and resources. JAMA Ophthalmol. 2014;132(9):1045–51.  https://doi.org/10.1001/jamaophthalmol.2014.1051.CrossRefPubMedGoogle Scholar
  11. 11.
    Scanlon PH. The English National Screening Programme for diabetic retinopathy 2003–2016. Acta Diabetol. 2017;54(6):515–25.  https://doi.org/10.1007/s00592-017-0974-1.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Prescott G, Sharp P, Goatman K, Scotland G, Fleming A, Philip S, et al. Improving the cost-effectiveness of photographic screening for diabetic macular oedema: a prospective, multi-centre, UK study. Br J Ophthalmol. 2014;98(8):1042–9.  https://doi.org/10.1136/bjophthalmol-2013-304338.CrossRefPubMedGoogle Scholar
  13. 13.
    Li HK, Horton M, Bursell S-E, Cavallerano J, Zimmer-Galler I, Tennant M, et al. Telehealth practice recommendations for diabetic retinopathy, second edition. Telemed J E Health. 2011;17(10):814–37.  https://doi.org/10.1089/tmj.2011.0075.CrossRefPubMedGoogle Scholar
  14. 14.
    Schulze-Döbold C, Erginay A, Robert N, Chabouis A, Massin P. Ophdiat(®): five-year experience of a telemedical screening programme for diabetic retinopathy in Paris and the surrounding area. Diabetes Metab. 2012;38(5):450–7.  https://doi.org/10.1016/j.diabet.2012.05.003.CrossRefPubMedGoogle Scholar
  15. 15.
    Cuadros J, Bresnick G. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J Diabetes Sci Technol. 2009;3(3):509–16.  https://doi.org/10.1177/193229680900300315.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zimmer-Galler I, Zeimer R. Results of implementation of the DigiScope for diabetic retinopathy assessment in the primary care environment. Telemed J E Health. 2006;12(2):89–98.  https://doi.org/10.1089/tmj.2006.12.89.CrossRefPubMedGoogle Scholar
  17. 17.
    Abramoff MD, Suttorp-Schulten MSA. Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. Telemed J E Health. 2005;11(6):668–74.  https://doi.org/10.1089/tmj.2005.11.668.CrossRefPubMedGoogle Scholar
  18. 18.
    Sanchez CR, Silva PS, Cavallerano JD, Aiello LP, Aiello LM. Ocular telemedicine for diabetic retinopathy and the Joslin Vision Network. Semin Ophthalmol. 2010;25(5-6):218–24.  https://doi.org/10.3109/08820538.2010.518893.CrossRefPubMedGoogle Scholar
  19. 19.
    Ng M, Nathoo N, Rudnisky CJ, Tennant MTS. Improving access to eye care: teleophthalmology in Alberta, Canada. J Diabetes Sci Technol. 2009;3(2):289–96.  https://doi.org/10.1177/193229680900300209.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Looker HC, Nyangoma SO, Cromie DT, Olson JA, Leese GP, Black MW, et al. Rates of referable eye disease in the Scottish National Diabetic Retinopathy Screening Programme. Br J Ophthalmol. 2014;98(6):790–5.  https://doi.org/10.1136/bjophthalmol-2013-303948.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Lagan MA, O’Gallagher MK, Johnston SE, Hart PM. Angle closure glaucoma in the Northern Ireland Diabetic Retinopathy Screening Programme. Eye. 2016;30(8):1091–3.  https://doi.org/10.1038/eye.2016.98.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Bursell SE, Cavallerano JD, Cavallerano AA, Clermont AC, Birkmire-Peters D, Aiello LP, et al. Stereo nonmydriatic digital-video color retinal imaging compared with Early Treatment Diabetic Retinopathy Study seven standard field 35-mm stereo color photos for determining level of diabetic retinopathy. Ophthalmology. 2001;108(3):572–85.  https://doi.org/10.1016/S0161-6420(00)00604-7.CrossRefPubMedGoogle Scholar
  23. 23.
    Cavallerano AA, Cavallerano JD, Katalinic P, Tolson AM, Aiello LP, Aiello LM, et al. Use of Joslin Vision Network digital-video nonmydriatic retinal imaging to assess diabetic retinopathy in a clinical program. Retina. 2003;23(2):215–23.  https://doi.org/10.1097/00006982-200304000-00013.CrossRefPubMedGoogle Scholar
  24. 24.
    Cavallerano JD, Aiello LP, Cavallerano AA, Katalinic P, Hock K, Kirby R, et al. Nonmydriatic digital imaging alternative for annual retinal examination in persons with previously documented no or mild diabetic retinopathy. Am J Ophthalmol. 2005;140(4):667–73.  https://doi.org/10.1016/j.ajo.2005.03.075.CrossRefPubMedGoogle Scholar
  25. 25.
    Kirkizlar E, Serban N, Sisson JA, Swann JL, Barnes CS, Williams MD. Evaluation of telemedicine for screening of diabetic retinopathy in the Veterans Health Administration. Ophthalmology. 2013;120(12):2604–10.  https://doi.org/10.1016/j.ophtha.2013.06.029.CrossRefPubMedGoogle Scholar
  26. 26.
    Kernt M, Hadi I, Pinter F, Seidensticker F, Hirneiss C, Haritoglou C, et al. Assessment of diabetic retinopathy using nonmydriatic ultra-widefield scanning laser ophthalmoscopy (Optomap) compared with ETDRS 7-field stereo photography. Diabetes Care. 2012;35(12):2459–63.  https://doi.org/10.2337/dc12-0346.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    • Silva PS, Cavallerano JD, Sun JK, Noble J, Aiello LM, Aiello LP. Nonmydriatic ultrawide field retinal imaging compared with dilated standard 7-field 35-mm photography and retinal specialist examination for evaluation of diabetic retinopathy. Am J Ophthalmol. 2012;154(3):549–559.e2. Nonmydriatic ultrawide field images are acquired more rapidly and compare favorably with gold standard dilated ETDRS photography and dilated fundus examination in determining diabetic retinopathy and diabetic macular edema severity.  https://doi.org/10.1016/j.ajo.2012.03.019.CrossRefPubMedGoogle Scholar
  28. 28.
    Kirkpatrick JN, Manivannan A, Gupta AK, Hipwell J, Forrester JV, Sharp PF. Fundus imaging in patients with cataract: role for a variable wavelength scanning laser ophthalmoscope. Br J Ophthalmol. 1995;79(10):892–9.  https://doi.org/10.1136/bjo.79.10.892.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Silva PS, Horton MB, Clary D, Lewis DG, Sun JK, Cavallerano JD, et al. Identification of diabetic retinopathy and ungradable image rate with ultrawide field imaging in a National Teleophthalmology Program. Ophthalmology. 2016;123(6):1360–7.  https://doi.org/10.1016/j.ophtha.2016.01.043.CrossRefPubMedGoogle Scholar
  30. 30.
    Wong RL, Tsang CW, Wong DS, McGhee S, Lam CH, Lian J, et al. Are we making good use of our public resources? The false-positive rate of screening by fundus photography for diabetic macular oedema. Hong Kong Med J. 2017;23(4):356–64.  https://doi.org/10.12809/hkmj166078.PubMedGoogle Scholar
  31. 31.
    Bruce BB, Lamirel C, Biousse V, Ward A, Heilpern KL, Newman NJ, et al. Feasibility of nonmydriatic ocular fundus photography in the emergency department: phase I of the FOTO-ED study. Acad Emerg Med. 2011;18(9):928–33.  https://doi.org/10.1111/j.1553-2712.2011.01147.x.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Ouyang Y, Heussen FM, Keane PA, Sadda SR, Walsh AC. The retinal disease screening study: retrospective comparison of nonmydriatic fundus photography and three-dimensional optical coherence tomography for detection of retinal irregularities. Invest Ophthalmol Vis Sci. 2013;54(8):5694–700.  https://doi.org/10.1167/iovs.13-12043.CrossRefPubMedGoogle Scholar
  33. 33.
    Li B, Powell A-M, Hooper PL, Sheidow TG. Prospective evaluation of teleophthalmology in screening and recurrence monitoring of neovascular age-related macular degeneration: a randomized clinical trial. JAMA Ophthalmol. 2015;133(3):276–82.  https://doi.org/10.1001/jamaophthalmol.2014.5014.CrossRefPubMedGoogle Scholar
  34. 34.
    Ausayakhun S, Skalet AH, Jirawison C, Ausayakhun S, Keenan JD, Khouri C, et al. Accuracy and reliability of telemedicine for diagnosis of cytomegalovirus retinitis. Am J Ophthalmol. 2011;152(6):1053–1058.e1.  https://doi.org/10.1016/j.ajo.2011.05.030.CrossRefPubMedGoogle Scholar
  35. 35.
    Jirawison C, Yen M, Leenasirimakul P, Chen J, Guadanant S, Kunavisarut P, et al. Telemedicine screening for cytomegalovirus retinitis at the point of care for human immunodeficiency virus infection. JAMA Ophthalmol. 2015;133(2):198–205.  https://doi.org/10.1001/jamaophthalmol.2014.4766.CrossRefPubMedGoogle Scholar
  36. 36.
    International Committee for the Classification of Retinopathy of Prematurity. The International Classification of Retinopathy of Prematurity revisited. Arch Ophthalmol. 2005;123:991–9.Google Scholar
  37. 37.
    Home Page - FocusROP. https://www.focusrop.com. Accessed 22 Oct 2017.
  38. 38.
    Fijalkowski N, Zheng LL, Henderson MT, Wallenstein MB, Leng T, Moshfeghi DM. Stanford University Network for Diagnosis of Retinopathy of Prematurity (SUNDROP): four-years of screening with telemedicine. Curr Eye Res. 2013;38(2):283–91.  https://doi.org/10.3109/02713683.2012.754902.CrossRefPubMedGoogle Scholar
  39. 39.
    Ells AL, Holmes JM, Astle WF, Williams G, Leske DA, Fielden M, et al. Telemedicine approach to screening for severe retinopathy of prematurity: a pilot study. Ophthalmology. 2003;110(11):2113–7.  https://doi.org/10.1016/S0161-6420(03)00831-5.CrossRefPubMedGoogle Scholar
  40. 40.
    Lorenz B, Spasovska K, Elflein H, Schneider N. Wide-field digital imaging based telemedicine for screening for acute retinopathy of prematurity (ROP). Six-year results of a multicentre field study. Graefes Arch Clin Exp Ophthalmol. 2009;247(9):1251–62.  https://doi.org/10.1007/s00417-009-1077-7.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Dai S, Chow K, Vincent A. Efficacy of wide-field digital retinal imaging for retinopathy of prematurity screening. Clin Exp Ophthalmol. 2011;39(1):23–9.  https://doi.org/10.1111/j.1442-9071.2010.02399.x.PubMedGoogle Scholar
  42. 42.
    Vinekar A, Gilbert C, Dogra M, Kurian M, Shainesh G, Shetty B, et al. The KIDROP model of combining strategies for providing retinopathy of prematurity screening in underserved areas in India using wide-field imaging, tele-medicine, non-physician graders and smart phone reporting. Indian J Ophthalmol. 2014;62(1):41–9.  https://doi.org/10.4103/0301-4738.126178.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Castillo-Riquelme MC, Lord J, Moseley MJ, Fielder AR, Haines L. Cost-effectiveness of digital photographic screening for retinopathy of prematurity in the United Kingdom. Int J Technol Assess Health Care. 2004;20(02):201–13.  https://doi.org/10.1017/S0266462304000984.CrossRefPubMedGoogle Scholar
  44. 44.
    • Chiang MF, Melia M, Buffenn AN, Lambert SR, Recchia FM, Simpson JL, et al. Detection of clinically significant retinopathy of prematurity using wide-angle digital retinal photography: a report by the American Academy of ophthalmology. Ophthalmology. 2012;119(6):1272–80. This report by the American Academy of Ophthalmology provides a detailed analysis and evaluation of the quality of evidence of studies related to the detection of clinically significant retinopathy of prematurity with wide-angle digital retinal photography.  https://doi.org/10.1016/j.ophtha.2012.01.002.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Fierson WM, Capone A Jr, American Academy of Pediatrics Section on Ophthalmology, American Academy of Ophthalmology, American Association of Certified Orthoptists. Telemedicine for evaluation of retinopathy of prematurity. Pediatrics. 2015;135(1):e238–54.  https://doi.org/10.1542/peds.2014-0978.CrossRefPubMedGoogle Scholar
  46. 46.
    Callaway NF, Ludwig CA, Blumenkranz MS, Jones JM, Fredrick DR, Moshfeghi DM. Retinal and optic nerve hemorrhages in the newborn infant: one-year results of the newborn eye screen test study. Ophthalmology. 2016;123(5):1043–52.  https://doi.org/10.1016/j.ophtha.2016.01.004.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Li L-H, Li N, Zhao J-Y, Fei P, Zhang G, Mao J, et al. Findings of perinatal ocular examination performed on 3573, healthy full-term newborns. Br J Ophthalmol. 2013;97(5):588–91.  https://doi.org/10.1136/bjophthalmol-2012-302539.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Vinekar A, Govindaraj I, Jayadev C, Kumar AK, Sharma P, Mangalesh S, et al. Universal ocular screening of 1021 term infants using wide-field digital imaging in a single public hospital in India—a pilot study. Acta Ophthalmol. 2015;93(5):e372–6.  https://doi.org/10.1111/aos.12685.CrossRefPubMedGoogle Scholar
  49. 49.
    Goyal P, Padhi TR, Das T, Pradhan L, Sutar S, Butola S, et al. Outcome of universal newborn eye screening with wide-field digital retinal image acquisition system: a pilot study. Eye. 2017;32(1):67–73.  https://doi.org/10.1038/eye.2017.129.CrossRefPubMedGoogle Scholar
  50. 50.
    Chee RI, Chan RVP. Universal newborn eye screening: an effective strategy to improve ocular health? Eye. 2017;32(1):50–2.  https://doi.org/10.1038/eye.2017.133.CrossRefPubMedGoogle Scholar
  51. 51.
    Kalpathy-Cramer J, Campbell JP, Erdogmus D, Tian P, Kedarisetti D, Moleta C, et al. Plus disease in retinopathy of prematurity: improving diagnosis by ranking disease severity and using quantitative image analysis. Ophthalmology. 2016;123(11):2345–51.  https://doi.org/10.1016/j.ophtha.2016.07.020.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Wittenberg LA, Jonsson NJ, RVP C, Chiang MF. Computer-based image analysis for plus disease diagnosis in retinopathy of prematurity. J Pediatr Ophthalmol Strabismus. 2012;49(1):11–9; quiz 10, 20.  https://doi.org/10.3928/01913913-20110222-01.CrossRefPubMedGoogle Scholar
  53. 53.
    Campbell JP, Kalpathy-Cramer J, Erdogmus D, Tian P, Kedarisetti D, Moleta C, et al. Plus disease in retinopathy of prematurity: a continuous spectrum of vascular abnormality as a basis of diagnostic variability. Ophthalmology. 2016;123(11):2338–44.  https://doi.org/10.1016/j.ophtha.2016.07.026.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Campbell JP, Ataer-Cansizoglu E, Bolon-Canedo V, Bozkurt A, Erdogmus D, Kalpathy-Cramer J, et al. Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis. JAMA Ophthalmol. 2016;134(6):651–7.  https://doi.org/10.1001/jamaophthalmol.2016.0611.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Ataer-Cansizoglu E, Bolon-Canedo V, Peter Campbell J, et al. Computer-based image analysis for plus disease diagnosis in retinopathy of prematurity: performance of the “i-ROP” system and image features associated with expert diagnosis. Transl Vis Sci Technol. 2015;4(6):5.  https://doi.org/10.1167/tvst.4.6.5.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Abbey AM, Besirli CG, Musch DC, Andrews CA, Capone A Jr, Drenser KA, et al. Evaluation of screening for retinopathy of prematurity by ROPtool or a lay reader. Ophthalmology. 2016;123(2):385–90.  https://doi.org/10.1016/j.ophtha.2015.09.048.CrossRefPubMedGoogle Scholar
  57. 57.
    Sim DA, Keane PA, Tufail A, Egan CA, Aiello LP, Silva PS. Automated retinal image analysis for diabetic retinopathy in telemedicine. Curr Diab Rep. 2015;15(3):14.  https://doi.org/10.1007/s11892-015-0577-6.CrossRefPubMedGoogle Scholar
  58. 58.
    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(11):1512–7.  https://doi.org/10.1136/bjo.2007.119453.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Abràmoff 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(3):351–7.  https://doi.org/10.1001/jamaophthalmol.2013.1743.CrossRefPubMedGoogle Scholar
  60. 60.
    Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124(3):343–51.  https://doi.org/10.1016/j.ophtha.2016.11.014.CrossRefPubMedGoogle Scholar
  61. 61.
    Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess. 2016;20(92):1–72.  https://doi.org/10.3310/hta20920.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.  https://doi.org/10.1038/nature14539.CrossRefPubMedGoogle Scholar
  63. 63.
    Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–6.  https://doi.org/10.1167/iovs.16-19964.CrossRefPubMedGoogle Scholar
  64. 64.
    • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. “In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy”.  https://doi.org/10.1001/jama.2016.17216.CrossRefPubMedGoogle Scholar
  65. 65.
    Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124(7):962–9.  https://doi.org/10.1016/j.ophtha.2017.02.008.CrossRefPubMedGoogle Scholar
  66. 66.
    Lee CS, Baughman DM, Lee AY. Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmology Retina. 2017;1(4):322–7.  https://doi.org/10.1016/j.oret.2016.12.009.CrossRefGoogle Scholar
  67. 67.
    Kalpathy-Cramer J, Peter Campbell J, Kim S, et al. Deep learning for the identification of plus disease in retinopathy of prematurity. Invest Ophthalmol Vis Sci. 2017;58:5554.Google Scholar
  68. 68.
    Peter Campbell J, Kim S, Swan R, et al. Is there clinical utility for a continuous severity score for plus disease in ROP? Invest Ophthalmol Vis Sci. 2017;58:4737.CrossRefGoogle Scholar
  69. 69.
    Tibrewal S, Tian P, Kedarisetti D, et al. Evaluation of computer-based image analysis for retinopathy of prematurity screening. Invest Ophthalmol Vis Sci. 2017;58:5539.Google Scholar
  70. 70.
    Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA. 2016;316(22):2366–7.  https://doi.org/10.1001/jama.2016.17563.CrossRefPubMedGoogle Scholar
  71. 71.
    Campbell JP, Swan R, Jonas K, et al. Implementation and evaluation of a tele-education system for the diagnosis of ophthalmic disease by international trainees. AMIA Annu Symp Proc. 2015;2015:366–75.PubMedPubMedCentralGoogle Scholar
  72. 72.
    Chan RVP, Patel SN, Ryan MC, et al. The Global Education Network for Retinopathy of Prematurity (Gen-Rop): development, implementation, and evaluation of a novel tele-education system (an American Ophthalmological Society thesis). Trans Am Ophthalmol Soc. 2015;113:T2.PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ru-ik Chee
    • 1
  • Dana Darwish
    • 1
  • Álvaro Fernández-Vega
    • 1
  • Samir N. Patel
    • 2
  • Karyn Jonas
    • 1
  • Susan Ostmo
    • 3
  • J. Peter Campbell
    • 3
  • Michael F. Chiang
    • 3
  • R.V. Paul Chan
    • 1
    Email author
  1. 1.Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear InfirmaryUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Department of OphthalmologyWills Eye HospitalPhiladelphiaUSA
  3. 3.Department of OphthalmologyCasey Eye Institute at Oregon Health & Science UniversityPortlandUSA

Personalised recommendations