The cost-effectiveness of an artificial intelligence software for stroke imaging and treatment decisions in English hospitals
Introduction Artificial intelligence-based imaging can support acute stroke treatment decisions, yet cost-effectiveness evidence spanning intravenous thrombolysis and mechanical thrombectomy pathways is limited. We evaluated cost-effectiveness of Brainomix 360 Stroke across National Health Service hospitals in England. Patients and Methods We developed a decision-analytic model from the National Health Service and personal social services perspective, combining a decision tree for acute stroke pathways with a long-term Markov model based on the modified Rankin Scale. The cohort comprised all acute ischaemic stroke admissions in England in one year (year = 2023, n=81,565 patients). Brainomix 360 Stroke was modelled as increasing the probability that eligible patients receive reperfusion therapy, based on a five-year real-world evaluation across 26 hospitals. Costs and quality-adjusted life years were discounted at 3.5% per year, with incremental net monetary benefit calculated at £20,000 per quality-adjusted life year. Results Brainomix 360 Stroke increased the number of patients receiving intravenous thrombolysis 8% and mechanical thrombectomy by 44%. The intervention was dominant, generating almost £4 million in cost savings and over 1,700 additional quality-adjusted life years, yielding an incremental net monetary benefit of over £38 million. Per AIS patient, this equates to £49 in cost savings and 0.021 additional quality-adjusted life years. Probabilistic sensitivity analysis indicated a 98.9% probability of cost-effectiveness at £20,000 per quality-adjusted life year. Results were robust across sensitivity and scenario analyses, though most sensitive to cohort age, long-term cost assumptions and treatment eligibility parameters. Conclusion Implementing artificial intelligence-assisted stroke imaging is highly likely to be cost-effective, driven by improved access to reperfusion therapies and better long-term functional outcomes.
Authors
- George Harston (ORCID: https://orcid.org/0000-0003-4916-5757)
- David R. Hargroves (ORCID: https://orcid.org/0000-0003-0127-3011)
- Gary A. Ford (ORCID: https://orcid.org/0000-0001-8719-4968)
- Zoe Woodhead (ORCID: https://orcid.org/0000-0003-0462-6791)
- Nichola R. Naylor (ORCID: https://orcid.org/0000-0003-4122-6631)
- Jeff Wyrtzen
- Deborah Lowe
Institutions
- East Kent Hospitals University NHS Foundation Trust (GB)
- University of Oxford (GB)
- Brainomix (United Kingdom) (GB)
- Oxford University Hospitals NHS Trust (GB)
- Wirral University Teaching Hospital NHS Foundation Trust (GB)
Publication Details
- Journal
- NeuroImage Stroke
- Published
- 2026-10-01
- DOI
- https://doi.org/10.1016/j.ynist.2026.100008
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00