Results from a Swedish model-based analysis of the cost-effectiveness of AI-assisted digital mammography
Johan Lyth, Pantelis Gialias, Magnus Husberg, Lars Bernfort, Tomas Bjerner, Maria Kristoffersen Wiberg, Lars-Åke Levin, Håkan Gustafsson
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of AI-assisted digital mammography (AI-DM) compared to conventional biennial breast cancer digital mammography screening (cDM) with double reading of screening mammograms, and to investigate the change in cost-effectiveness based on four different sub-strategies of AI-DM. MATERIALS AND METHODS: A decision-analytic state-transition Markov model was used to analyse the decision of whether to use cDM or AI-DM in breast cancer screening. In this Markov model, one-year cycles were used, and the analysis was performed from a healthcare perspective with a lifetime horizon. In the model, we analysed 1000 hypothetical individuals attending mammography screenings assessed with AI-DM compared with 1000 hypothetical individuals assessed with cDM. RESULTS: The total costs, including both screening-related costs and breast cancer-related costs, were €3,468,967 and €3,528,288 for AI-DM and cDM, respectively. AI-DM resulted in a cost saving of €59,320 compared to cDM. Per 1000 individuals, AI-DM gained 10.8 quality-adjusted life years (QALYs) compared to cDM. Gained QALYs at a lower cost means that the AI-DM screening strategy was dominant compared to cDM. Break-even occurred at the second screening at age 42 years. CONCLUSION: This analysis showed that AI-assisted mammography for biennial breast cancer screening in a Swedish population of women aged 40-74 years is a cost-saving strategy compared to a conventional strategy using double human screen reading. Further clinical studies are needed, as scenario analyses showed that other strategies, more dependent on AI, are also cost-saving. KEY POINTS: Question To evaluate the cost-effectiveness of AI-DM in comparison to conventional biennial breast cDM screening. Findings AI-DM is cost-effective, and the break-even point occurred at the second screening at age 42 years. Clinical relevance The implementation of AI is clearly cost-effective as it reduces the total cost for the healthcare system and simultaneously results in a gain in QALYs.