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Towards artificial intelligence for clinical stroke care

Nature Reviews Neurology volume 16, pages 5–6 (2020)Cite this article

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Artificial intelligence algorithms are well suited to the fast decision making needed in the management of large vessel occlusive stroke. In a new study, a fully automated CT angiography algorithm identified large vessel occlusions with impressive sensitivity, but the work highlights the need for high reporting standards to maximize translatability.

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Fig. 1: An example of a colour-coded class activation ‘attention map’.

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Authors and Affiliations

  1. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Thabele M. Leslie-Mazwi

  2. Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Thabele M. Leslie-Mazwi

  3. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Michael H. Lev

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  1. Thabele M. Leslie-Mazwi
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  2. Michael H. Lev
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Correspondence to Michael H. Lev.

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Competing interests

M.H.L. has received consultancy fees from GE Healthcare and Takeda Pharmaceuticals, and has received research support from GE Healthcare. He has also applied for patents for CT haemorrhage and head CT ischaemia detection technology, transformed domain machine learning, and electrical impedance spectroscopy detection of haemorrhage. T.M.L.-M. declares no competing interests.

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Leslie-Mazwi, T.M., Lev, M.H. Towards artificial intelligence for clinical stroke care. Nat Rev Neurol 16, 5–6 (2020). https://doi.org/10.1038/s41582-019-0287-9

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