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.

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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
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article

The cost-effectiveness of an artificial intelligence software for stroke imaging and treatment decisions in English hospitals

George Harston, David R. Hargroves, Gary A. Ford, Zoe Woodhead et al.
NeuroImage Stroke
Acute Ischemic Stroke Management
article

The cost-effectiveness of an artificial intelligence software for stroke imaging and treatment decisions in English hospitals

George Harston, David R. Hargroves, Gary A. Ford, Zoe Woodhead, Nichola R. Naylor, Jeff Wyrtzen, Deborah Lowe
article en

Abstract

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.

NeuroImage Stroke
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)
Openalex Percentile: Top 11%
Acute Ischemic Stroke Management
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