Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment
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 so...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-09-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-021-01077-4 |
| _version_ | 1819122575422259200 |
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| author | Kicky G. van Leeuwen Frederick J. A. Meijer Steven Schalekamp Matthieu J. C. M. Rutten Ewoud J. van Dijk Bram van Ginneken Tim M. Govers Maarten de Rooij |
| author_facet | Kicky G. van Leeuwen Frederick J. A. Meijer Steven Schalekamp Matthieu J. C. M. Rutten Ewoud J. van Dijk Bram van Ginneken Tim M. Govers Maarten de Rooij |
| author_sort | Kicky G. van Leeuwen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T06:54:38Z |
| format | Article |
| id | doaj.art-9021ccd5090b43c9a364aa1e09cfe198 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-12-22T06:54:38Z |
| publishDate | 2021-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-9021ccd5090b43c9a364aa1e09cfe1982022-12-21T18:35:00ZengSpringerOpenInsights into Imaging1869-41012021-09-011211910.1186/s13244-021-01077-4Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessmentKicky G. van Leeuwen0Frederick J. A. Meijer1Steven Schalekamp2Matthieu J. C. M. Rutten3Ewoud J. van Dijk4Bram van Ginneken5Tim M. Govers6Maarten de Rooij7Department of Medical Imaging, Radboud University Medical CenterDepartment of Medical Imaging, Radboud University Medical CenterDepartment of Medical Imaging, Radboud University Medical CenterDepartment of Medical Imaging, Radboud University Medical CenterDepartment of Neurology, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University Medical CenterDepartment of Medical Imaging, Radboud University Medical CenterDepartment of Operating Rooms, Radboud University Medical CenterDepartment of Medical Imaging, Radboud University Medical CenterAbstract 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.https://doi.org/10.1186/s13244-021-01077-4StrokeArtificial intelligenceCost–benefit analysisComputed tomography angiographyEndovascular procedures |
| spellingShingle | Kicky G. van Leeuwen Frederick J. A. Meijer Steven Schalekamp Matthieu J. C. M. Rutten Ewoud J. van Dijk Bram van Ginneken Tim M. Govers Maarten de Rooij Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment Insights into Imaging Stroke Artificial intelligence Cost–benefit analysis Computed tomography angiography Endovascular procedures |
| title | Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment |
| title_full | Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment |
| title_fullStr | Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment |
| title_full_unstemmed | Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment |
| title_short | Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment |
| title_sort | cost effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke an early health technology assessment |
| topic | Stroke Artificial intelligence Cost–benefit analysis Computed tomography angiography Endovascular procedures |
| url | https://doi.org/10.1186/s13244-021-01077-4 |
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