[Skip to Content]
[Skip to Content Landing]

Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes

Educational Objective
To assess the cost-effectiveness of detecting and treating diabetic retinopathy (DR) and its sequelae among children with type 1 diabetes and type 2 diabetes using artificial intelligence (AI) DR screening vs standard screening by an eye care professional.
1 Credit CME
Key Points

Question  Is autonomous artificial intelligence diabetic retinopathy screening more cost-effective than standard eye care screening examination performed by health care professionals?

Findings  This economic evaluation used decision analysis to model the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with type 1 and type 2 diabetes and found an incremental cost-effectiveness ratio of $31 for type 1 diabetes and $95 for type 2 diabetes for each additional case of diabetic retinopathy identified compared with standard practice.

Meaning  These results suggest that when more than 23% of patients adhere to diabetic retinopathy screening recommendations, autonomous artificial intelligence screening is the preferred strategy and cost-saving for the patient and family.


Importance  Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI) has become available, providing immediate results in the clinic setting, but the cost-effectiveness of this strategy compared with standard examination is unknown.

Objective  To assess the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with T1D and T2D using AI diabetic retinopathy screening vs standard screening by an eye care professional (ECP).

Design, Setting, and Participants  In this economic evaluation, parameter estimates were obtained from the literature from 1994 to 2019 and assessed from March 2019 to January 2020. Parameters included out-of-pocket cost for autonomous AI screening, ophthalmology visits, and treating diabetic retinopathy; probability of undergoing standard retinal examination; relative odds of undergoing screening; and sensitivity, specificity, and diagnosability of the ECP screening examination and autonomous AI screening.

Main Outcomes and Measures  Costs or savings to the patient based on mean patient payment for diabetic retinopathy screening examination and cost-effectiveness based on costs or savings associated with the number of true-positive results identified by diabetic retinopathy screening.

Results  In this study, the expected true-positive proportions for standard ophthalmologic screening by an ECP were 0.006 for T1D and 0.01 for T2D, and the expected true-positive proportions for autonomous AI were 0.03 for T1D and 0.04 for T2D. The base case scenario of 20% adherence estimated that use of autonomous AI would result in a higher mean patient payment ($8.52 for T1D and $10.85 for T2D) than conventional ECP screening ($7.91 for T1D and $8.20 for T2D). However, autonomous AI screening was the preferred strategy when at least 23% of patients adhered to diabetic retinopathy screening.

Conclusions and Relevance  These results suggest that point-of-care diabetic retinopathy screening using autonomous AI systems is effective and cost saving for children with diabetes and their caregivers at recommended adherence rates.

Sign in to take quiz and track your certificates

Buy This Activity

JN Learning™ is the home for CME and MOC from the JAMA Network. Search by specialty or US state and earn AMA PRA Category 1 Credit(s)™ from articles, audio, Clinical Challenges and more. Learn more about CME/MOC

CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: July 16, 2020.

Corresponding Author: Risa M. Wolf, MD, Department of Pediatrics, Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, 200 N Wolfe St, Baltimore, MD 21287 (rwolf@jhu.edu).

Published Online: September 3, 2020. doi:10.1001/jamaophthalmol.2020.3190

Author Contributions: Drs Wolf and Lehmann had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Wolf, Abramoff, Lehmann.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Abramoff, Lehmann.

Obtained funding: Abramoff.

Administrative, technical, or material support: Wolf, Channa.

Supervision: Channa, Abramoff.

Conflict of Interest Disclosures: Dr Abramoff reported being the founder, executive chairman, director, and an investor in Digital Diagnostics (formerly IDx) and having a patent assigned to Digital Diagnostics and University of Iowa that is relevant to the content of this article. No other disclosures were reported.

Additional Contributions: Steve Downs, MD, Wake Forest University, Winston-Salem, North Carolina, reviewed the structure of the model and was not compensated for this help.

Dabelea  D , Stafford  JM , Mayer-Davis  EJ ,  et al; SEARCH for Diabetes in Youth Research Group.  Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood.   JAMA. 2017;317(8):825-835. doi:10.1001/jama.2017.0686 PubMedGoogle ScholarCrossref
Forga  L , Goñi  MJ , Ibáñez  B , Cambra  K , García-Mouriz  M , Iriarte  A .  Influence of age at diagnosis and time-dependent risk factors on the development of diabetic retinopathy in patients with type 1 diabetes.   J Diabetes Res. 2016;2016:9898309. doi:10.1155/2016/9898309 PubMedGoogle Scholar
Tapley  JL , McGwin  G  Jr , Ashraf  AP ,  et al.  Feasibility and efficacy of diabetic retinopathy screening among youth with diabetes in a pediatric endocrinology clinic: a cross-sectional study.   Diabetol Metab Syndr. 2015;7:56. doi:10.1186/s13098-015-0054-z PubMedGoogle ScholarCrossref
Thomas  RL , Dunstan  FD , Luzio  SD ,  et al.  Prevalence of diabetic retinopathy within a national diabetic retinopathy screening service.   Br J Ophthalmol. 2015;99(1):64-68. doi:10.1136/bjophthalmol-2013-304017 PubMedGoogle ScholarCrossref
Wang  SY , Andrews  CA , Gardner  TW , Wood  M , Singer  K , Stein  JD .  Ophthalmic screening patterns among youths with diabetes enrolled in a large US managed care network.   JAMA Ophthalmol. 2017;135(5):432-438. doi:10.1001/jamaophthalmol.2017.0089 PubMedGoogle ScholarCrossref
Klein  R , Klein  BE , Moss  SE , Cruickshanks  KJ .  The Wisconsin Epidemiologic Study of diabetic retinopathy, XIV: ten-year incidence and progression of diabetic retinopathy.   Arch Ophthalmol. 1994;112(9):1217-1228. doi:10.1001/archopht.1994.01090210105023 PubMedGoogle ScholarCrossref
Zhang  X , Saaddine  JB , Chou  CF ,  et al.  Prevalence of diabetic retinopathy in the United States, 2005-2008.   JAMA. 2010;304(6):649-656. doi:10.1001/jama.2010.1111 PubMedGoogle ScholarCrossref
Conlin  PR , Fisch  BM , Cavallerano  AA , Cavallerano  JD , Bursell  SE , Aiello  LM .  Nonmydriatic teleretinal imaging improves adherence to annual eye examinations in patients with diabetes.   J Rehabil Res Dev. 2006;43(6):733-740. doi:10.1682/JRRD.2005.07.0117 PubMedGoogle ScholarCrossref
Mansberger  SL , Gleitsmann  K , Gardiner  S ,  et al.  Comparing the effectiveness of telemedicine and traditional surveillance in providing diabetic retinopathy screening examinations: a randomized controlled trial.   Telemed J E Health. 2013;19(12):942-948. doi:10.1089/tmj.2012.0313 PubMedGoogle ScholarCrossref
Whited  JD , Datta  SK , Aiello  LM ,  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(6):641-651. doi:10.1089/tmj.2005.11.641 PubMedGoogle ScholarCrossref
Kolomeyer  AM , Nayak  NV , Simon  MA ,  et al.  Feasibility of retinal screening in a pediatric population with type 1 diabetes mellitus.   J Pediatr Ophthalmol Strabismus. 2014;51(5):299-306. doi:10.3928/01913913-20140709-01 PubMedGoogle ScholarCrossref
Roser  P , Kalscheuer  H , Groener  JB ,  et al.  Diabetic retinopathy screening ratio is improved when using a digital, nonmydriatic fundus camera onsite in a diabetes outpatient clinic.   J Diabetes Res. 2016;2016:4101890. doi:10.1155/2016/4101890 PubMedGoogle Scholar
Stillman  JK , Gole  GA , Wootton  R ,  et al.  Telepaediatrics and diabetic retinopathy screening of young people with diabetes in Queensland.   J Telemed Telecare. 2004;10(suppl 1):92-94. doi:10.1258/1357633042614203 PubMedGoogle ScholarCrossref
Bursell  SE , Cavallerano  JD , Cavallerano  AA ,  et al; Joslin Vision Network Research Team.  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):572-585. doi:10.1016/S0161-6420(00)00604-7 PubMedGoogle ScholarCrossref
Abràmoff  MD , Lavin  PT , Birch  M , Shah  N , Folk  JC .  Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.   NPJ Digit Med. 2018;1:39. doi:10.1038/s41746-018-0040-6 PubMedGoogle ScholarCrossref
American Diabetes Association.  Microvascular complications and foot care: standards of medical care in diabetes-2019.   Diabetes Care. 2019;42(suppl 1):S124-S138. doi:10.2337/dc19-S011 PubMedGoogle ScholarCrossref
Eaddy  MT , Cook  CL , O’Day  K , Burch  SP , Cantrell  CR .  How patient cost-sharing trends affect adherence and outcomes: a literature review.   P T. 2012;37(1):45-55.PubMedGoogle Scholar
Philips  Z , Bojke  L , Sculpher  M , Claxton  K , Golder  S .  Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment.   Pharmacoeconomics. 2006;24(4):355-371. doi:10.2165/00019053-200624040-00006 PubMedGoogle ScholarCrossref
Husereau  D , Drummond  M , Petrou  S ,  et al; CHEERS Task Force.  Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement.   Int J Technol Assess Health Care. 2013;29(2):117-122. doi:10.1017/S0266462313000160 PubMedGoogle ScholarCrossref
American Diabetes Association.  Children and adolescents: standards of medical care in diabetes-2019.   Diabetes Care. 2019;42(suppl 1):S148-S164. doi:10.2337/dc19-S013 PubMedGoogle ScholarCrossref
VanderBeek  BL , Scavelli  K , Yu  Y .  Determinants in initial treatment choice for diabetic macular edema.   Ophthalmol Retina. 2019;4(1):41-48. doi:10.1016/j.oret.2019.05.016PubMedGoogle ScholarCrossref
Javitt  JC , Aiello  LP .  Cost-effectiveness of detecting and treating diabetic retinopathy.   Ann Intern Med. 1996;124(1, pt 2):164-169. doi:10.7326/0003-4819-124-1_Part_2-199601011-00017 PubMedGoogle ScholarCrossref
Wittenborn  JS , Zhang  X , Feagan  CW ,  et al; Vision Cost-Effectiveness Study Group.  The economic burden of vision loss and eye disorders among the United States population younger than 40 years.   Ophthalmology. 2013;120(9):1728-1735. doi:10.1016/j.ophtha.2013.01.068 PubMedGoogle ScholarCrossref
An  J , Niu  F , Turpcu  A , Rajput  Y , Cheetham  TC .  Adherence to the American Diabetes Association retinal screening guidelines for population with diabetes in the United States.   Ophthalmic Epidemiol. 2018;25(3):257-265. doi:10.1080/09286586.2018.1424344 PubMedGoogle ScholarCrossref
Benoit  SR , Swenor  B , Geiss  LS , Gregg  EW , Saaddine  JB .  Eye care utilization among insured people with diabetes in the U.S., 2010-2014.   Diabetes Care. 2019;42(3):427-433. doi:10.2337/dc18-0828 PubMedGoogle ScholarCrossref
Mansberger  SL , Sheppler  C , Barker  G ,  et al.  Long-term comparative effectiveness of telemedicine in providing diabetic retinopathy screening examinations: a randomized clinical trial.   JAMA Ophthalmol. 2015;133(5):518-525. doi:10.1001/jamaophthalmol.2015.1 PubMedGoogle ScholarCrossref
Crossland  L , Askew  D , Ware  R ,  et al.  Diabetic retinopathy screening and monitoring of early stage disease in Australian general practice: tackling preventable blindness within a chronic care model.   J Diabetes Res. 2016;2016:8405395. doi:10.1155/2016/8405395 PubMedGoogle Scholar
Group  TS ; TODAY Study Group.  Retinopathy in youth with type 2 diabetes participating in the TODAY clinical trial.   Diabetes Care. 2013;36(6):1772-1774. doi:10.2337/dc12-2387 PubMedGoogle ScholarCrossref
Lin  DY , Blumenkranz  MS , Brothers  RJ , Grosvenor  DM .  The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography.   Am J Ophthalmol. 2002;134(2):204-213. doi:10.1016/S0002-9394(02)01522-2 PubMedGoogle ScholarCrossref
Pugh  JA , Jacobson  JM , Van Heuven  WA ,  et al.  Screening for diabetic retinopathy: the wide-angle retinal camera.   Diabetes Care. 1993;16(6):889-895. doi:10.2337/diacare.16.6.889 PubMedGoogle ScholarCrossref
Liu  Y , Rajamanickam  VP , Parikh  RS ,  et al.  Diabetic retinopathy assessment variability among eye care providers in an urban teleophthalmology program.   Telemed J E Health. 2019;25(4):301-308. doi:10.1089/tmj.2018.0019 PubMedGoogle ScholarCrossref
Verbraak  FD , Abramoff  MD , Bausch  GCF ,  et al.  Diagnostic accuracy of a device for the automated detection of diabetic retinopathy in a primary care setting.   Diabetes Care. 2019;42(4):651-656. doi:10.2337/dc18-0148 PubMedGoogle ScholarCrossref
Davis  RM , Fowler  S , Bellis  K , Pockl  J , Al Pakalnis  V , Woldorf  A .  Telemedicine improves eye examination rates in individuals with diabetes: a model for eye-care delivery in underserved communities.   Diabetes Care. 2003;26(8):2476. doi:10.2337/diacare.26.8.2476 PubMedGoogle ScholarCrossref
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(2):318-322. doi:10.2337/diacare.28.2.318 PubMedGoogle ScholarCrossref
Detsky  AS , Naglie  G , Krahn  MD , Redelmeier  DA , Naimark  D .  Primer on medical decision analysis, part 2: building a tree.   Med Decis Making. 1997;17(2):126-135. doi:10.1177/0272989X9701700202 PubMedGoogle ScholarCrossref
Thornton Snider  J , Seabury  S , Lopez  J , McKenzie  S , Goldman  DP .  Impact of type 2 diabetes medication cost sharing on patient outcomes and health plan costs.   Am J Manag Care. 2016;22(6):433-440.PubMedGoogle Scholar
Gibson  TB , Song  X , Alemayehu  B ,  et al.  Cost sharing, adherence, and health outcomes in patients with diabetes.   Am J Manag Care. 2010;16(8):589-600.PubMedGoogle Scholar
AMA CME Accreditation Information

Credit Designation Statement: The American Medical Association designates this Journal-based CME activity activity for a maximum of 1.00  AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to:

  • 1.00 Medical Knowledge MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program;;
  • 1.00 Self-Assessment points in the American Board of Otolaryngology – Head and Neck Surgery’s (ABOHNS) Continuing Certification program;
  • 1.00 MOC points in the American Board of Pediatrics’ (ABP) Maintenance of Certification (MOC) program;
  • 1.00 Lifelong Learning points in the American Board of Pathology’s (ABPath) Continuing Certification program; and
  • 1.00 CME points in the American Board of Surgery’s (ABS) Continuing Certification program

It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting MOC credit.

Want full access to the AMA Ed Hub?
After you sign up for AMA Membership, make sure you sign in or create a Physician account with the AMA in order to access all learning activities on the AMA Ed Hub
Buy this activity
Want full access to the AMA Ed Hub?
After you sign up for AMA Membership, make sure you sign in or create a Physician account with the AMA in order to access all learning activities on the AMA Ed Hub
Buy this activity
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience
Education Center Collection Sign In Modal Right

Name Your Search

Save Search
With a personal account, you can:
  • Access free activities and track your credits
  • Personalize content alerts
  • Customize your interests
  • Fully personalize your learning experience

Lookup An Activity


My Saved Searches

You currently have no searches saved.


My Saved Courses

You currently have no courses saved.