Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Department of Pediatrics, Oregon Health & Science University, Portland.
JAMA Ophthalmol. 2022 Apr 1;140(4):401-409. doi: 10.1001/jamaophthalmol.2022.0223.
Artificial intelligence (AI)-based retinopathy of prematurity (ROP) screening may improve ROP care, but its cost-effectiveness is unknown.
To evaluate the relative cost-effectiveness of autonomous and assistive AI-based ROP screening compared with telemedicine and ophthalmoscopic screening over a range of estimated probabilities, costs, and outcomes.
DESIGN, SETTING, AND PARTICIPANTS: A cost-effectiveness analysis of AI ROP screening compared with ophthalmoscopy and telemedicine via economic modeling was conducted. Decision trees created and analyzed modeled outcomes and costs of 4 possible ROP screening strategies: ophthalmoscopy, telemedicine, assistive AI with telemedicine review, and autonomous AI with only positive screen results reviewed. A theoretical cohort of infants requiring ROP screening in the United States each year was analyzed.
Screening and treatment costs were based on Current Procedural Terminology codes and included estimated opportunity costs for physicians. Outcomes were based on the Early Treatment of ROP study, defined as timely treatment, late treatment, or correctly untreated. Incremental cost-effectiveness ratios were calculated at a willingness-to-pay threshold of $100 000. One-way and probabilistic sensitivity analyses were performed comparing AI strategies to telemedicine and ophthalmoscopy to evaluate the cost-effectiveness across a range of assumptions. In a secondary analysis, the modeling was repeated and assumed a higher sensitivity for detection of severe ROP using AI compared with ophthalmoscopy.
This theoretical cohort included 52 000 infants born 30 weeks' gestation or earlier or weighed 1500 g or less at birth. Autonomous AI was as effective and less costly than any other screening strategy. AI-based ROP screening was cost-effective up to $7 for assistive and $34 for autonomous screening compared with telemedicine and $64 and $91 compared with ophthalmoscopy in the primary analysis. In the probabilistic sensitivity analysis, autonomous AI screening was more than 60% likely to be cost-effective at all willingness-to-pay levels vs other modalities. In a second simulated cohort with 99% sensitivity for AI, the number of late treatments for ROP decreased from 265 when ROP screening was performed with ophthalmoscopy to 40 using autonomous AI.
AI-based screening for ROP may be more cost-effective than telemedicine and ophthalmoscopy, depending on the added cost of AI and the relative performance of AI vs human examiners detecting severe ROP. As AI-based screening for ROP is commercialized, care must be given to appropriately price the technology to ensure its benefits are fully realized.
基于人工智能（AI）的早产儿视网膜病变（ROP）筛查可能会改善 ROP 护理，但它的成本效益尚不清楚。
在一系列估计的概率、成本和结果范围内，评估自主和辅助基于 AI 的 ROP 筛查与远程医疗和检眼镜筛查相比的相对成本效益。
设计、设置和参与者：通过经济建模对 AI ROP 筛查与检眼镜和远程医疗进行了成本效益分析。决策树创建并分析了 4 种可能的 ROP 筛查策略的模型结果和成本：检眼镜、远程医疗、带远程医疗审查的辅助 AI 和仅对阳性筛查结果进行审查的自主 AI。对美国每年需要 ROP 筛查的理论婴儿队列进行了分析。
筛查和治疗成本基于当前程序术语代码，并包括医生的估计机会成本。结果基于早期治疗 ROP 研究，定义为及时治疗、晚期治疗或正确未治疗。在愿意支付 10 万美元的阈值下计算增量成本效益比。对 AI 策略与远程医疗和检眼镜进行了单向和概率敏感性分析，以评估在一系列假设下的成本效益。在二次分析中，建模重复，并假设使用 AI 检测严重 ROP 的敏感性高于检眼镜。
这个理论队列包括 52000 名出生 30 周或更早或出生体重 1500 克或更轻的婴儿。自主 AI 与任何其他筛查策略一样有效且成本更低。在主要分析中，与远程医疗相比，辅助 AI 的筛查成本效益高达 7 美元，自主 AI 的筛查成本效益高达 34 美元；与检眼镜相比，辅助 AI 的筛查成本效益高达 64 美元，自主 AI 的筛查成本效益高达 91 美元。在概率敏感性分析中，自主 AI 筛查在所有支付意愿水平上都超过 60%具有成本效益，而与其他方式相比。在第二个模拟队列中，AI 的敏感性为 99%，当使用检眼镜进行 ROP 筛查时，ROP 的晚期治疗数量从 265 例减少到使用自主 AI 时的 40 例。
基于 AI 的 ROP 筛查可能比远程医疗和检眼镜更具成本效益，具体取决于 AI 的附加成本和 AI 检测严重 ROP 的性能与人类检查者相比。随着基于 AI 的 ROP 筛查的商业化，必须注意为该技术定价，以确保充分实现其效益。
