Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis

Background We aimed to evaluate the cost-effectiveness of an artificial intelligence -(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non -Indigenous and Indigenous people living with diabetes in Australia. Methods We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decisionanalytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non -Indigenous and 65,160 Indigenous Australians living with diabetes aged >= 20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI -based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit -cost ratio (BCR), and net monetary benefits (NMB). A Willingness -to -pay (WTP) threshold of AU$50,000 per quality -adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings With the status quo, the non -Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost -saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost -saving for the Indigenous population. Notably, universal AI -based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation Our findings suggest that implementing AI -based DR screening in primary care is highly effective and cost -saving in both Indigenous and non -Indigenous populations. Copyright (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).