基于社区的人工智能眼底疾病筛查的成本效益和成本效用：来自中国上海的建模研究。

Cost-effectiveness and cost-utility of community-based blinding fundus diseases screening with artificial intelligence: A modelling study from Shanghai, China.

机构信息

Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, No. 1440, Hongqiao Road, Shanghai, China; National Clinical Research Center for Eye Diseases, No. 1440, Hongqiao Road, Shanghai, China; Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, No. 1440, Hongqiao Road, Shanghai, China.

出版信息

Comput Biol Med. 2024 Dec;183:109329. doi: 10.1016/j.compbiomed.2024.109329. Epub 2024 Nov 2.

DOI:10.1016/j.compbiomed.2024.109329

PMID:39489106

Abstract

BACKGROUND

With application of artificial intelligence (AI) in the disease screening, process reengineering occurred simultaneously. Whether process reengineering deserves special emphasis in AI implementation in the community-based blinding fundus diseases screening is not clear.

METHOD

Cost-effectiveness and cost-utility analyses were performed employing decision-analytic Markov models. A hypothetical cohort of community residents was followed in the model over a period of 30 1-year Markov cycles, starting from the age of 60. The simulated cohort was based on work data of the Shanghai Digital Eye Disease Screening program (SDEDS). Three scenarios were compared: centralized screening with manual grading-based telemedicine systems (Scenario 1), centralized screening with an AI-assisted screening system (Scenario 2), and process reengineered screening with an AI-assisted screening system (Scenario 3). The main outcomes were incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR).

RESULTS

Compared with Scenario 1, Scenario 2 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $ 490010.62 per 10,000 people screened, with an ICER of $2619.98 per year of blindness avoided and an ICUR of $4589.13 per QALY. Compared with Scenario 1, Scenario 3 results in incremental 187.03 years of blindness avoided and incremental 106.78 QALYs at an additional cost of $242313.23 per 10,000 people screened, with an ICER of $1295.60 per year of blindness avoided and an ICUR of $2269.35 per QALY. Although Scenario 2 and 3 could be considered cost-effective, the screening cost of Scenario 3 was 27.6 % and the total cost was 1.1 % lower, with the same expected effectiveness and utility. The probabilistic sensitivity analyses show that Scenario 3 dominated 69.1 % and 70.3 % of simulations under one and three times the local GDP per capita thresholds.

CONCLUSIONS

AI can improve the cost-effectiveness and cost-utility of screenings, especially when process reengineering is performed. Therefore, process reengineering is strongly recommended when AI is implemented.

摘要

背景

随着人工智能（AI）在疾病筛查中的应用，同时也发生了流程再造。在社区为基础的盲眼病筛查中，AI 实施过程中是否需要特别强调流程再造尚不清楚。

方法

采用决策分析马尔可夫模型进行成本效益和成本效用分析。在模型中，假设一个社区居民队列在 30 个 1 年马尔可夫周期中进行随访，起始年龄为 60 岁。模拟队列基于上海数字化眼病筛查计划（SDEDS）的工作数据。比较了三种方案：基于人工分级远程医疗系统的集中筛查（方案 1）、基于 AI 辅助筛查系统的集中筛查（方案 2）和基于 AI 辅助筛查系统的流程再造筛查（方案 3）。主要结果是增量成本效益比（ICER）和增量成本效用比（ICUR）。

结果

与方案 1 相比，方案 2 在每 10000 人筛查中额外增加 490010.62 美元的成本，可避免 187.03 年的失明，并增加 106.78 个质量调整生命年（QALY），增量成本效益比为每避免 1 年失明 2619.98 美元，每增加 1 个 QALY 成本为 4589.13 美元。与方案 1 相比，方案 3 在每 10000 人筛查中额外增加 242313.23 美元的成本，可避免 187.03 年的失明，并增加 106.78 个 QALY，增量成本效益比为每避免 1 年失明 1295.60 美元，每增加 1 个 QALY 成本为 2269.35 美元。虽然方案 2 和 3 可以被认为是具有成本效益的，但方案 3 的筛查成本降低了 27.6%，总成本降低了 1.1%，而预期效果和效用相同。概率敏感性分析表明，在当地人均 GDP 阈值的 1 倍和 3 倍下，方案 3 分别占模拟的 69.1%和 70.3%。