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. 2021 Sep 25;12(1):133.
doi: 10.1186/s13244-021-01077-4.

Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment

Kicky G van Leeuwen  1 , Frederick J A Meijer  2 , Steven Schalekamp  2 , Matthieu J C M Rutten  2   3 , Ewoud J van Dijk  4 , Bram van Ginneken  2 , Tim M Govers  5 , Maarten de Rooij  2
Affiliations

Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment

Kicky G van Leeuwen et al. Insights Imaging. .

Abstract

Background: Limited evidence is available on the clinical impact of artificial intelligence (AI) in radiology. Early health technology assessment (HTA) is a methodology to assess the potential value of an innovation at an early stage. We use early HTA to evaluate the potential value of AI software in radiology. As a use-case, we evaluate the cost-effectiveness of AI software aiding the detection of intracranial large vessel occlusions (LVO) in stroke in comparison to standard care. We used a Markov based model from a societal perspective of the United Kingdom predominantly using stroke registry data complemented with pooled outcome data from large, randomized trials. Different scenarios were explored by varying missed diagnoses of LVOs, AI costs and AI performance. Other input parameters were varied to demonstrate model robustness. Results were reported in expected incremental costs (IC) and effects (IE) expressed in quality adjusted life years (QALYs).

Results: Applying the base case assumptions (6% missed diagnoses of LVOs by clinicians, $40 per AI analysis, 50% reduction of missed LVOs by AI), resulted in cost-savings and incremental QALYs over the projected lifetime (IC: - $156, - 0.23%; IE: + 0.01 QALYs, + 0.07%) per suspected ischemic stroke patient. For each yearly cohort of patients in the UK this translates to a total cost saving of $11 million.

Conclusions: AI tools for LVO detection in emergency care have the potential to improve healthcare outcomes and save costs. We demonstrate how early HTA may be applied for the evaluation of clinically applied AI software for radiology.

Keywords: Artificial intelligence; Computed tomography angiography; Cost–benefit analysis; Endovascular procedures; Stroke.

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Conflict of interest statement

KGvL, FJAM, SS, MJCMR, EJvD, TMG, MdR declare that they have no competing interests. BvG is co-founder of and receives royalties from Thirona and receives royalties from Delft Imaging and Mevis Medical Solutions. These disclosures are unrelated to the submitted work.

Figures

Fig. 1
Fig. 1
Decision tree applicable to the standard of care strategy and the AI tool strategy. In the AI tool strategy the ratio of occlusions (not) detected was altered. CTA, computed tomography angiography, IAT, intra-arterial thrombectomy
Fig. 2
Fig. 2
Scenario analysis demonstrating incremental costs. Incremental costs at varying prices for the AI tool per analysis ($0–$200) and varying percentage of reduction of missed large vessel occlusion diagnoses (0–100%). Green circles demonstrate a cost reduction whereas red circles signify an increase in costs. The size of the circle is related to the height of the incremental costs
Fig. 3
Fig. 3
Results of one-way sensitivity analysis. The effect of varying several parameters is shown. In the left diagram the impact on the costs is demonstrated and in the right diagram the impact on the QALYs. Light gray bars represent lower bounds, dark gray bars upper bounds. The axis intersects at the base case results of − $156 and 0.0095 QALY. LVO, large vessel occlusion; IAT, intra-arterial thrombectomy; QALY, quality-adjusted life-year
See this image and copyright information in PMC

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