Department of Ophthalmology, the First Affiliated Hospital, Jinan University, Guangzhou, China.
Institute of Ophthalmology, School of Medicine, Jinan University, Guangzhou, China.
BMC Health Serv Res. 2022 Feb 25;22(1):260. doi: 10.1186/s12913-022-07655-6.
Diabetic 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.
We 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.
From 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).
Our 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.
糖尿病视网膜病变（DR）已成为糖尿病的一种微血管并发症，成为全球失明的主要原因。建议糖尿病患者定期进行糖尿病视网膜病变筛查，以便及时进行治疗，降低视力损害的发生率。然而，由于缺乏眼科保健设施，尤其是在中国农村地区，DR 筛查工作并未得到很好的开展。基于人工智能（AI）的 DR 筛查已成为一种新策略，其在敏感性和特异性方面表现出很有前景的诊断性能，缓解了设施和眼科医生短缺的压力，因为它可以快速准确地诊断。在本研究中，我们基于马尔可夫模型估计了中国农村地区 AI 筛查 DR 的成本效益，为推广 AI 筛查 DR 提供了证据。
我们估计了 AI 筛查的成本效益，并将其与由眼科医生评估眼底图像的眼科医生筛查进行了比较。我们开发了一种基于马尔可夫模型的混合决策树，以分析 AI 筛查策略相对于不筛查策略和眼科医生筛查策略（占主导地位）在 35 年内（中国农村糖尿病患者的平均预期寿命）的成本、效果和增量成本效益比（ICER）。分析从卫生系统角度（包括直接医疗费用）和社会角度（包括医疗和非医疗费用）进行。效果通过质量调整生命年来分析。通过进行单因素敏感性分析和概率分析来估计结果的稳健性。
从卫生系统角度来看，与不筛查相比，AI 筛查和眼科医生筛查的增量成本分别为 180.19 美元和 215.05 美元，但 QALYs 更多。AI 筛查的 ICER 为 1107.63 美元。从考虑所有直接和间接成本的社会角度来看，与不筛查相比，AI 筛查的 ICER 为 10347.12 美元，低于成本效益阈值（2019 年中国人均国内生产总值的 1-3 倍）。
我们的分析表明，基于人工智能的筛查比传统的眼科医生筛查更具成本效益，有望成为中国农村地区 DR 筛查的替代方法。
