Cost‐effectiveness simulation of AI screening for DR in Japan

Abstract

Aims: We investigated the efficacy of AI‐based screenings in preventing blindness, specifically assessing cost‐effectiveness when two further detection opportunities are incorporated: “DR screening during national health checkups” and “DR screening during diabetes clinic visits at a physician's office”, or a combination of the two. We also examined potential additional costs associated with AI screening under various conditions.Methods: We employed a Markov Model in this simulation study. We constructed the modelling of diabetes detection, treatment, and retinopathy treatment based on transition probabilities derived from epidemiological and clinical studies. These covered incidence and progression rates of diabetes and diabetic retinopathy, treatment options and their efficacy, and visual prognosis. The age range for the model was set at 40–90 years with a 1‐year cycle, assuming a population of 500 000 individuals with a discount rate of 2%. Simulations were conducted using TreeAge. Costs were calculated using the direct costs of general health examinations and insurance treatments. We assumed diabetic retinopathy would result in a consistent visual acuity following detection, progression, and treatment, providing utility values based on the severity of DR.Results: DR screening via AI in health check‐up programs proved not to be cost‐effective, as it led to a 0.9% increase in cumulative blindness and the ICERs were dominated. The addition of an annual AI screening at physicians' clinics, with referrals to ophthalmology based on the results, resulted in an ICER of 8192751JPY/QALY, which was not cost‐effective. However, assuming that 75% of patients received AI screening during medical examinations and that 75% of these underwent ophthalmological examinations per the results, AI screening proved to be dominant and cost‐effective, reducing the cumulative number of blindness by 2.8%.Conclusions: Even though automated AI diagnosis of DR is becoming a reality, our findings suggest that merely introducing AI screening in health checkups and physicians' clinics in Japan will not be cost‐effective. However, cost‐effectiveness can be improved by increasing the rate of screening and the follow‐up rate of ophthalmological examinations. If this is achieved, there might also be a possibility of incurring additional costs due to AI screening. Our study underlines the importance of a seamless link from diabetes detection to ophthalmologic care for successful DR screening with AI systems.