AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025140025
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REVIEW ARTICLE

Applications of artificial intelligence in acute stroke imaging

Arjun Kalyanpur1 ,  Neetika Mathur2*
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1 Department of Clinical Radiology, dAIgnostiX/Teleradiology Solutions, Bengaluru, Karnataka, India
2 Department of Clinical Training and Research, dAIgnostiX/Teleradiology Solutions, Bengaluru, Karnataka, India
AIH, 025140025 https://doi.org/10.36922/AIH025140025
Received: 31 March 2025 | Revised: 17 June 2025 | Accepted: 4 July 2025 | Published online: 24 July 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Stroke remains a major global public health challenge, representing the second leading cause of death worldwide and a primary contributor to long-term disability. The paradigm “time is brain” underscores the importance of treating stroke patients within the critical window period, ideally within 60 min from symptom onset, to minimize damage and improve outcomes. The integration of artificial intelligence (AI) into stroke imaging has transformed diagnosis and management by increasing speed, accuracy, and efficiency. AI algorithms have been trained to detect acute stroke, assess hemorrhage, detect and quantify midline shifts, calculate automated Alberta Stroke Program Early Computed Tomography Scores, and identify dense middle cerebral artery on non-contrast computed tomography (CT) as well as large vessel occlusions on CT angiograms, with high sensitivity and specificity. AI also aids in treatment guidance and outcome monitoring. This review provides insights into AI applications in acute stroke imaging, including its role in early detection, screening, triage and prioritization, automated image analysis, workflow optimization, and system integration. Despite its benefits, AI adoption faces challenges such as clinical validation, ethical considerations, and integration into existing workflows. Future developments depend on large, diverse, and well-annotated datasets to train more robust AI systems capable of guiding treatment strategies and improving patient outcomes. The seamless integration of cloud-based AI solutions with telereporting platforms has the potential to revolutionize stroke care by enabling rapid, high-quality radiologic interpretation, even in remote locations.

Keywords
Artificial intelligence
Stroke imaging
Computed tomography angiograms
Magnetic resonance angiography
Hemorrhage
Teleradiology
Workflow integration
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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