Abstract
BackgroundDiabetic retinopathy (DR) has become a leading cause of global blindness as a microvascular complication of diabetes. Regular screening of diabetic retinopathy is strongly recommended for people with diabetes so that timely treatment can be provided to reduce the incidence of visual impairment. However, DR screening is not well carried out due to lack of eye care facilities, especially in the rural areas of China. Artificial intelligence (AI) based DR screening has emerged as a novel strategy and show promising diagnostic performance in sensitivity and specificity, relieving the pressure of the shortage of facilities and ophthalmologists because of its quick and accurate diagnosis. In this study, we estimated the cost-effectiveness of AI screening for DR in rural China based on Markov model, providing evidence for extending use of AI screening for DR.MethodsWe estimated the cost-effectiveness of AI screening and compared it with ophthalmologist screening in which fundus images are evaluated by ophthalmologists. We developed a Markov model-based hybrid decision tree to analyze the costs, effectiveness and incremental cost-effectiveness ratio (ICER) of AI screening strategies relative to no screening strategies and ophthalmologist screening strategies (dominated) over 35 years (mean life expectancy of diabetes patients in rural China). The analysis was conducted from the health system perspective (included direct medical costs) and societal perspective (included medical and nonmedical costs). Effectiveness was analyzed with quality-adjusted life years (QALYs). The robustness of results was estimated by performing one-way sensitivity analysis and probabilistic analysis.ResultsFrom the health system perspective, AI screening and ophthalmologist screening had incremental costs of $180.19 and $215.05 but more quality-adjusted life years (QALYs) compared with no screening. AI screening had an ICER of $1,107.63. From the societal perspective which considers all direct and indirect costs, AI screening had an ICER of $10,347.12 compared with no screening, below the cost-effective threshold (1–3 times per capita GDP of Chinese in 2019).ConclusionsOur analysis demonstrates that AI-based screening is more cost-effective compared with conventional ophthalmologist screening and holds great promise to be an alternative approach for DR screening in the rural area of China.
Highlights
Diabetic retinopathy (DR) has become a leading cause of global blindness as a microvascular compli‐ cation of diabetes
In China, the performance of a deep learning systems (DLS) model was evaluated for screening pre-proliferative diabetic retinopathy and diabetic macular edema, and the results showed a sensitivity of 97% and a specificity of 91% based on 19,900 images [14]
The incremental cost-effectiveness ratio (ICER) of Artificial intelligence (AI) screening compared with no screening group was $1,107.63/quality-adjusted life years (QALYs) gained, less than the threshold of $30,765.09, which was three times Chinese per capita gross domestic product (GDP) in 2019
Summary
Diabetic retinopathy (DR) has become a leading cause of global blindness as a microvascular compli‐ cation of diabetes. Considering the importance of regular screening of people with diabetes for timely intervention and reduction of vision impairment [9], it is urgent to take measures to make DR screening more available and affordable in the rural areas of China. In this aspect, a new kind of screening strategy for DR incorporating artificial intelligence technology has great potential, especially in low- and middleincome countries [10]
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