Artificial intelligence for decision support in acute stroke — current roles and potential

Abstract

The identification and treatment of patients with stroke is becoming increasingly complex as more treatment options become available and new relationships between disease features and treatment response are continually discovered. Consequently, clinicians must constantly learn new skills (such as clinical evaluations or image interpretation), stay up to date with the literature and incorporate advances into everyday practice. The use of artificial intelligence (AI) to support clinical decision making could reduce inter-rater variation in routine clinical practice and facilitate the extraction of vital information that could improve identification of patients with stroke, prediction of treatment responses and patient outcomes. Such support systems would be ideal for centres that deal with few patients with stroke or for regional hubs, and could assist informed discussions with the patients and their families. Moreover, the use of AI for image processing and interpretation in stroke could provide any clinician with an imaging assessment equivalent to that of an expert. However, any AI-based decision support system should allow for expert clinician interaction to enable identification of errors (for example, in automated image processing). In this Review, we discuss the increasing importance of imaging in stroke management before exploring the potential and pitfalls of AI-assisted treatment decision support in acute stroke.

Key points

  • Imaging-based treatment guidance has been demonstrated as an effective approach in patients with a suspected stroke.

  • Clinical trials in which imaging is not used for patient selection are likely to include many patients with minor stroke or stroke mimics, making treatment effects difficult to detect.

  • Artificial intelligence (AI) and machine learning could provide image interpretation that equals or exceeds that of experts and could collate key features to assist clinicians with treatment decisions.

  • AI could be used to generate estimations of likely patient outcomes, which would not only be useful for assisting treatment decisions but also for informing family discussions.

  • AI-based decision assistance systems could be especially useful for centres without dedicated stroke specialists.

  • Any decision assistance tools must be validated and applied appropriately, and clear guidelines are needed to define how useful systems are in clinical practice.

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Fig. 1: Perfusion imaging outputs from validated automated post-processing software.
Fig. 2: Treatment decision making by humans alone and in combination with artificial intelligence.
Fig. 3: Irreversible injury in ischaemic stroke.
Fig. 4: Identification of patients who are unlikely to benefit from therapy.
Fig. 5: Clinical implications of poor image acquisition.
Fig. 6: A poorly acquired acute CT perfusion scan.

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All authors contributed to all aspects of the manuscript.

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Correspondence to Mark Parsons.

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M.P. and A.B. have active research collaborations with Siemens Healthineers, Canon medical Systems and Apollo Medical Imaging. L.C. declares no competing interests.

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Nature Reviews Neurology thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Bivard, A., Churilov, L. & Parsons, M. Artificial intelligence for decision support in acute stroke — current roles and potential. Nat Rev Neurol 16, 575–585 (2020). https://doi.org/10.1038/s41582-020-0390-y

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