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  • AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study

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New devices and techniques
Original research
AI software detection of large vessel occlusion stroke on CT angiography: a real-world prospective diagnostic test accuracy study
  1. Stavros Matsoukas1,
  2. Jacob Morey1,
  3. Gregory Lock1,
  4. Deeksha Chada1,
  5. Tomoyoshi Shigematsu1,
  6. Naoum Fares Marayati1,
  7. Bradley N Delman2,
  8. Amish Doshi2,
  9. Shahram Majidi1,
  10. Reade De Leacy1,
  11. Christopher Paul Kellner1,
  12. Johanna T Fifi1,2,3
  1. 1 Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  2. 2 Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  3. 3 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  1. Correspondence to Dr Stavros Matsoukas, Department of Neurosurgery, The Mount Sinai Hospital, New York, New York, USA; stavros.matsoukas{at}mountsinai.org

Abstract

Background Artificial intelligence (AI) software is increasingly applied in stroke diagnostics. However, the actual performance of AI tools for identifying large vessel occlusion (LVO) stroke in real time in a real-world setting has not been fully studied.

Objective To determine the accuracy of AI software in a real-world, three-tiered multihospital stroke network.

Methods All consecutive head and neck CT angiography (CTA) scans performed during stroke codes and run through an AI software engine (Viz LVO) between May 2019 and October 2020 were prospectively collected. CTA readings by radiologists served as the clinical reference standard test and Viz LVO output served as the index test. Accuracy metrics were calculated.

Results Of a total of 1822 CTAs performed, 190 occlusions were identified; 142 of which were internal carotid artery terminus (ICA-T), middle cerebral artery M1, or M2 locations. Accuracy metrics were analyzed for two different groups: ICA-T and M1 ±M2. For the ICA-T/M1 versus the ICA-T/M1/M2 group, sensitivity was 93.8% vs 74.6%, specificity was 91.1% vs 91.1%, negative predictive value was 99.7% vs 97.6%, accuracy was 91.2% vs 89.8%, and area under the curve was 0.95 vs 0.86, respectively. Detection rates for ICA-T, M1, and M2 occlusions were 100%, 93%, and 49%, respectively. As expected, the algorithm offered better detection rates for proximal occlusions than for mid/distal M2 occlusions (58% vs 28%, p=0.03).

Conclusions These accuracy metrics support Viz LVO as a useful adjunct tool in stroke diagnostics. Fast and accurate diagnosis with high negative predictive value mitigates missing potentially salvageable patients.

  • brain
  • CT angiography
  • device
  • stroke
  • technology

Data availability statement

Data are available upon reasonable request. N/A.

https://doi.org/10.1136/neurintsurg-2021-018391

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    Data availability statement

    Data are available upon reasonable request. N/A.

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    Footnotes

    • Twitter @moreyjr917, @faresmarayati, @rdeleacymd, @chriskellnerMD

    • Contributors JTF and StM conceptualized the project. StM and JM were responsible for gathering and organizing the data. StM and DC conducted the analysis. BND and AD provided the radiology reports. GL and TS served as adjudicating reviewers. StM and JM drafted the manuscript. All authors made edits and approved the final manuscript. StM and JTF revised the manuscript.JTF serves as the guarantor author for this project.

    • Funding JTF and CPK have received research funding from Viz.ai.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.