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Artificial intelligence software for diagnosing intracranial arterial occlusion in patients with acute ischemic stroke

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

To evaluate the diagnostic performance of AI software in diagnosing intracranial arterial occlusions in the proximal anterior circulation at CT angiography (CTA) and to compare it to manual reading performed in clinical practice.

Methods

Patients with acute ischemic stroke underwent CTA to detect arterial occlusion in the proximal anterior circulation. Retrospective review of CTA scans by two neuroradiologists served as reference standard. Sensitivity and specificity of AI software (StrokeViewer) were compared to those of manual reading using the McNemar test. The proportions of correctly detected occlusions in the distal internal carotid artery and/or M1 segment of the middle cerebral artery (large vessel occlusion [LVO]) and in the M2 segment of the middle cerebral artery (medium vessel occlusion [MeVO]) were calculated.

Results

Of the 474 patients, 75 (15.8%) had an arterial occlusion in the proximal anterior circulation according to the reference standard. Sensitivity of StrokeViewer software was not significantly different compared to that of manual reading (77.3% vs. 78.7%, P = 1.000). Specificity of StrokeViewer software was significantly lower than that of manual reading (88.5% vs. 100%, P < 0.001). StrokeViewer software correctly identified 40 of 42 LVOs (95.2%) and 18 of 33 MeVOs (54.5%). StrokeViewer software detected 8 of 16 (50%) intracranial arterial occlusions which were missed by manual reading.

Conclusion

The current AI software detected intracranial arterial occlusion with moderate sensitivity and fairly high specificity. The AI software may detect additional occlusions which are missed by manual reading. As such, the use of AI software may be of value in clinical stroke care.

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Acknowledgements

We thank Razmara Nizak, Alex Puiu, and Merel Boers from NICO.LAB, Amsterdam, the Netherlands, for their technical support.

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Authors and Affiliations

  1. Department of Radiology, Zuyderland Medical Center, Henri Dunantstraat 5, 6419 PC, Heerlen/Sittard/Geleen, The Netherlands

    Bram A. C. M. Fasen & Robert M. Kwee

  2. Department of Medical Physics, Zuyderland Medical Center, Heerlen/Sittard/Geleen, The Netherlands

    Ralph C. M. Berendsen

Authors
  1. Bram A. C. M. Fasen
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  2. Ralph C. M. Berendsen
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  3. Robert M. Kwee
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Correspondence to Robert M. Kwee.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

This retrospective study was approved by the institutional review board of our hospital (IRB number Z2019102) and patients’ consents were waived.

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Fasen, B.A.C.M., Berendsen, R.C.M. & Kwee, R.M. Artificial intelligence software for diagnosing intracranial arterial occlusion in patients with acute ischemic stroke. Neuroradiology 64, 1579–1583 (2022). https://doi.org/10.1007/s00234-022-02912-1

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  • DOI: https://doi.org/10.1007/s00234-022-02912-1

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