June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Integration of Artificial Intelligence into a Telemedicine-Based Diabetic Retinopathy Screening Program
Author Affiliations & Notes
  • Karen Chen
    Stanford Medicine, Stanford, California, United States
  • Eliot R Dow
    Stanford Medicine, Stanford, California, United States
  • Nergis C Khan
    Stanford Medicine, Stanford, California, United States
  • Marcie Levine
    Stanford Health Care, Stanford, California, United States
  • Chandrashan Perera
    Stanford Medicine, Stanford, California, United States
  • Anuradha Phadke
    Stanford Health Care, Stanford, California, United States
  • Jimmy Dang
    Stanford Health Care, Stanford, California, United States
  • Kirsti Weng
    Stanford Health Care, Stanford, California, United States
  • Diana V Do
    Stanford Medicine, Stanford, California, United States
  • Vinit B Mahajan
    Stanford Medicine, Stanford, California, United States
  • Prithvi Mruthyunjaya
    Stanford Medicine, Stanford, California, United States
  • Kapil Mishra
    Stanford Medicine, Stanford, California, United States
  • Theodore Leng
    Stanford Medicine, Stanford, California, United States
  • David Myung
    Stanford Medicine, Stanford, California, United States
  • Footnotes
    Commercial Relationships   Karen Chen None; Eliot Dow None; Nergis Khan None; Marcie Levine None; Chandrashan Perera None; Anuradha Phadke None; Jimmy Dang None; Kirsti Weng None; Diana Do None; Vinit Mahajan None; Prithvi Mruthyunjaya None; Kapil Mishra None; Theodore Leng None; David Myung None
  • Footnotes
    Support   The study was funded by an unrestricted grant from Research to Prevent Blindness and a P30 NIH grant to the Byers Eye Institute of Stanford University.
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 1422 – A0118. doi:
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      Karen Chen, Eliot R Dow, Nergis C Khan, Marcie Levine, Chandrashan Perera, Anuradha Phadke, Jimmy Dang, Kirsti Weng, Diana V Do, Vinit B Mahajan, Prithvi Mruthyunjaya, Kapil Mishra, Theodore Leng, David Myung; Integration of Artificial Intelligence into a Telemedicine-Based Diabetic Retinopathy Screening Program. Invest. Ophthalmol. Vis. Sci. 2022;63(7):1422 – A0118.

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

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Abstract

Purpose : Annual diabetic retinopathy (DR) screenings are critical for preventing blindness in patients with diabetes. Our goal was to successfully integrate artificial intelligence-based image interpretation software into a telemedicine-based diabetic retinopathy screening program at primary care clinics.

Methods : The Byers Eye Institute at Stanford (BEIS) partnered with University HealthCare Alliance (UHA) to integrate IDx-DR, an FDA-cleared AI diagnostic system that autonomously detects diabetic retinopathy (DR) in fundus images, into a pre-existing teleophthalmology workflow. Patients without a prior DR diagnosis or a DR exam in the past year were offered the opportunity to have retinal photographs taken at the end of their primary care visit. The AI-human hybrid workflow involves interpretation by the IDx system. Images deemed ungradable by the AI software were then sent for interpretation by a retina specialist at the BEIS reading center. Patients were referred for in-person exam if either the AI or the human reader detected more than mild DR (mtmDR) in the images.

Results : From April 2021 to December 2021, a total of 550 patients with diabetes at four primary care sites opted for DR screening using the AI-human hybrid workflow. Of these, 72 patients screened positive for mild or worse DR (13%) and 425 patients screened negative (77%). Average gradeability ranged by site from between 80% and 90%.

At each site, the percentage of the diabetic patient population up-to-date with recommended eye exams was measured in accordance with the HEDIS measure specification for Comprehensive Diabetes Care (CDC). A target of 67.89% was chosen, reflecting the 90th percentile HEDIS national benchmark. The percentage of patients adherent with annual diabetic eye exams reached a peak of 71.1% across the four sites after integration of AI into the telemedicine workflow. These outcomes demonstrate that integration of the AI-human hybrid workflow resulted in increased patient adherence with annual diabetic eye exams.

Conclusions : An AI-human hybrid workflow for detecting referral-warranted DR was successfully implemented in the primary care setting and resulted in improved patient adherence and quality measures associated with annual diabetic eye exams.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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