Cost-effectiveness of AI-based diabetic retinopathy screening in nationwide health checkups and diabetes management in Japan: A modeling study

IF 7.4 3区医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes research and clinical practice Pub Date : 2025-03-01 Epub Date: 2025-01-30 DOI:10.1016/j.diabres.2025.112015

Yoko Akune , Ryo Kawasaki , Rei Goto , Hiroshi Tamura , Yoshimune Hiratsuka , Masakazu Yamada

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Abstract

Aims

We evaluated the cost-effectiveness of artificial intelligence (AI)-based diabetic retinopathy (DR) screening in Japan. This evaluation compared the simultaneous introduction of AI in nationwide health checkups, namely “specific health check-ups in Japan” (SHC), and diabetes complication management (AI-case) with the current situation where AI is not being introduced (conventional-case) from the healthcare payer’s perspective.

Methods

A cost-effectiveness analysis was conducted using a new individual-based state transition model. Model parameters, including the incidence and progression of DR, health utility values, and costs of screening and treatment, were based on literature data and expert opinion. The analysis estimated quality-adjusted life years (QALYs), cumulative costs, and incremental cost-effectiveness ratios (ICER).

Results

The ICER comparing the AI-case with conventional-case was estimated to be JPY 1,598,244/QALY (USD 11,375/QALY), which is below the willingness-to-pay threshold of JPY 5 million/QALY (USD 35,584/QALY). Scenario analyses revealed that ICERs for the AI-based DR screening in SHC-only condition was JPY 1,895,226/QALY (USD 13,488/QALY) and JPY 3,960,839/QALY (USD 28,189/QALY) in diabetes management-only condition.

Conclusions

The introduction of AI-based DR screening for SHC and diabetes management was cost-effective compared to the current situation in Japan.

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基于人工智能的糖尿病视网膜病变筛查在日本全国健康检查和糖尿病管理中的成本效益：一项模型研究

目的：我们评估了日本基于人工智能（AI）的糖尿病视网膜病变（DR）筛查的成本效益。该评价比较了在全国健康检查中同时引入人工智能的情况，即“日本特定健康检查”（SHC）和糖尿病并发症管理（AI-case），以及从医疗保健付款人的角度来看，目前没有引入人工智能的情况（常规病例）。方法：采用一种新的基于个体的状态转移模型进行成本-效果分析。模型参数，包括DR的发病率和进展，健康效用值，以及筛查和治疗的成本，是基于文献数据和专家意见。该分析估计了质量调整寿命年（QALYs）、累积成本和增量成本-效果比（ICER）。结果：人工智能病例与常规病例的ICER估计为1,598,244日元/QALY(11,375美元/QALY)，低于500万日元/QALY（35,584美元/QALY）的支付意愿阈值。情景分析显示，仅在shc条件下，基于人工智能的DR筛查的ICERs为1,895,226日元/QALY(13,488美元/QALY)，仅在糖尿病管理条件下，ICERs为3,960,839日元/QALY（28,189美元/QALY）。结论：与日本目前的情况相比，引入基于人工智能的DR筛查用于SHC和糖尿病管理具有成本效益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diabetes research and clinical practice 医学-内分泌学与代谢

CiteScore

10.30

自引率

3.90%

发文量

862

审稿时长

32 days

期刊介绍： Diabetes Research and Clinical Practice is an international journal for health-care providers and clinically oriented researchers that publishes high-quality original research articles and expert reviews in diabetes and related areas. The role of the journal is to provide a venue for dissemination of knowledge and discussion of topics related to diabetes clinical research and patient care. Topics of focus include translational science, genetics, immunology, nutrition, psychosocial research, epidemiology, prevention, socio-economic research, complications, new treatments, technologies and therapy.