Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program

Abstract

Purpose: We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows. Patients and Methods: A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow (“Human Workflow”), an AI-based workflow involving IDx-DR (“AI Workflow”), and a two-step hybrid workflow (“AI–Human Hybrid Workflow”). The AI Workflow provided same-day results, whereas the other workflows took 1– 5 days. Patients were surveyed by phone about follow-up decisions. Results: Under the AI Workflow, 279 patients screened positive for MTMDR. Of these, 69.2% followed up with an ophthalmologist within 90 days. Altogether 70.5% ( N =48) of patients who followed up chose their location based on primary care referral. Under the Human Workflow and AI–Human Hybrid Workflow, 12.0% ( N =14/117) and 11.7% ( N =12/103) of patients with a referrable screening result followed up compared to 35.5% of patients under the AI Workflow ( N =99/279; χ 2 df=2 = 36.70, p < 0.00000001). Conclusion: Ophthalmology follow-up after a positive DR screening result is approximately three-fold higher under the AI Workflow than either the Human Workflow or AI–Human Hybrid Workflow. Improved follow-up behavior may be due to the decreased time to screening result. Keywords: telemedicine, teleophthalmology, fundus photography, deep learning, machine learning, referral

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