Emerging role of artificial intelligence in stroke imaging

ABSTRACT

Introduction: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefore, clinicians must regularly learn new skill, stay up to date with the literature and integrate advances into daily practice. The application of artificial intelligence (AI) to assist clinical decision making could diminish inter-rater variation in routine clinical practice and accelerate the mining of vital data that could expand recognition of patients with stroke, forecast of treatment responses and patient outcomes.

Areas covered: In this review, the authors provide an up-to-date review of AI in stroke, analyzing the latest papers on this subject. These have been divided in two main groups: stroke diagnosis and outcome prediction.

Expert opinion: The highest value of AI is its capability to merge, select and condense a large amount of clinical and imaging features of a single patient and to associate these with fitted models that have gone through robust assessment and optimization with large cohorts of data to support clinical decision making

KEYWORDS:

• Supervised artificial intelligence
• stroke
• perfusion imaging
• image optimization and analysis

Article Highlights

• Imaging-based treatment assistance has been validated as an useful approach in patients with a clinically suspected stroke.

• Artificial intelligence (AI) and machine learning could provide image analysis that corresponds or surpasses that of neuroradiologists and could codify additional key features to assist clinicians with treatment decisions.

• AI could be used to produce appraisals of patient outcomes, which could be useful for aiding treatment choices.

• The highest value of AI is its capability to merge, select and condense a large amount of clinical and imaging features of a single patient and to associate these with fitted models that have gone through robust assessment and optimization with large cohorts of data to support clinical decision making

• AI-based decision support technologies could be particularly useful for centres without dedicated neuroradiologist.

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Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.