July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Real-World Use of Artificial Intelligence to Screen for Diabetic Retinopathy at Diabetes Care Clinics
Author Affiliations & Notes
  • E Simon Barriga
    VisionQuest Biomedical, Albuquerque, New Mexico, United States
  • Jeremy Benson
    VisionQuest Biomedical, Albuquerque, New Mexico, United States
    Computer Science, The University of New Mexico, Albuquerque, New Mexico, United States
  • Gilberto Zamora
    VisionQuest Biomedical, Albuquerque, New Mexico, United States
  • Javier Lozano
    Clinicas del Azucar, Monterrey, Mexico
  • Sheila C Nemeth
    VisionQuest Biomedical, Albuquerque, New Mexico, United States
  • Peter Soliz
    VisionQuest Biomedical, Albuquerque, New Mexico, United States
  • Footnotes
    Commercial Relationships   E Simon Barriga, VisionQuest Biomedical, LLC (I); Jeremy Benson, VisionQuest Biomedical, LLC (E); Gilberto Zamora, VisionQuest Biomedical, LLC (E); Javier Lozano, VisionQuest Biomedical, LLC (C); Sheila Nemeth, VisionQuest Biomedical, LLC (E); Peter Soliz, VisionQuest Biomedical, LLC (I)
  • Footnotes
    Support  NIH Grant EY018280
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 5462. doi:https://doi.org/
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      E Simon Barriga, Jeremy Benson, Gilberto Zamora, Javier Lozano, Sheila C Nemeth, Peter Soliz; Real-World Use of Artificial Intelligence to Screen for Diabetic Retinopathy at Diabetes Care Clinics. Invest. Ophthalmol. Vis. Sci. 2019;60(9):5462. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose : To present clinical performance results of an automatic diabetic retinopathy (DR) tele-screening system operating for the past two years at a network of comprehensive diabetic care clinics.

Methods : VisionQuest has introduced EyeStar, an artificial intelligence (AI) DR screening system which is being used at thirteen comprehensive diabetes care clinics in Monterrey, Mexico. Since September 2016, a total of 12,427 patients with diabetes have been screened for the presence of DR using the EyeStar system. Retinal images are captured by a nurse or medical technician using Canon CR-2 or Volk’s Pictor Plus retinal cameras. Images are uploaded to a central server and are processed and returned by EyeStar to the clinics within 5 minutes. The EyeStar system returns an output of “non-refer” for cases with no DR, Mild non-proliferative DR (NPDR), or Moderate NPDR; or “refer to ophthalmology” for cases with Severe NPDR, proliferative DR (PDR), or suspect for clinically significant macular edema.
To evaluate the performance of EyeStar, we sample 20% of the cases which are then over-read by a certified retinal grader. The performance of EyeStar is measured in terms of sensitivity, specificity, and predictive values.

Results : N=11,951 patients (96%) were imaged with Canon cameras and N=476 (4%) with Volk cameras. Sensitivity of EyeStar for referable DR is 98% with a specificity of 77%. EyeStar’ negative predictive value (NPV) is 99.9% and its positive predictive value (PPV) is 21.2%. Workload reduction, defined as the percent of total cases that did not need referral to an ophthalmologist, is 77%.

Conclusions : The application of AI to DR screening has been a topic of extensive research, however, very few examples of real-world applications have been reported so far. The EyeStar system demonstrated clinically safe and effective performance for the detection of diabetic retinopathy in a clinical setting for the past two years. The software screens out 77% of patients who do not need a referral to an ophthalmologist while maintaining high levels of safety. Resource scarcity and limited access to care make automation a viable alternative to screen their diabetic population.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.


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