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Artificial Intelligence in Health

REVIEW ARTICLE
Applications of artificial intelligence in acute

stroke imaging

2
Arjun Kalyanpur 1 and Neetika Mathur *
1 Department of Clinical Radiology, dAIgnostiX/Teleradiology Solutions, Bengaluru, Karnataka, India
2 Department of Clinical Training and Research, dAIgnostiX/Teleradiology Solutions, Bengaluru,
Karnataka, India

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
*Corresponding author: in treatment guidance and outcome monitoring. This review provides insights into AI
Neetika Mathur applications in acute stroke imaging, including its role in early detection, screening,
(neetika.mathur@imagecorelab. triage and prioritization, automated image analysis, workflow optimization, and
com)
system integration. Despite its benefits, AI adoption faces challenges such as clinical
Citation: Kalyanpur A, Mathur N. validation, ethical considerations, and integration into existing workflows. Future
LLMs-Healthcare: Applications
of artificial intelligence in acute developments depend on large, diverse, and well-annotated datasets to train more
stroke imaging. Artif Intell Health. robust AI systems capable of guiding treatment strategies and improving patient
2025;2(4):1-12. outcomes. The seamless integration of cloud-based AI solutions with telereporting
doi: 10.36922/AIH025140025
platforms has the potential to revolutionize stroke care by enabling rapid, high-
Received: March 31, 2025 quality radiologic interpretation, even in remote locations.
Revised: June 17, 2025
Accepted: July 4, 2025 Keywords: Artificial intelligence; Stroke imaging; Computed tomography angiograms;
Magnetic resonance angiography; Hemorrhage; Teleradiology; Workflow integration
Published online: July 24, 2025
Copyright: © 2025 Author(s).
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution 1. Introduction
License, permitting distribution,
and reproduction in any medium, Stroke is the second leading cause of mortality and a major global health concern,
provided the original work is responsible for about 5.5 million deaths annually, and was the fourth‐highest Level
properly cited. 3 cause of disability‐adjusted life years (DALYs) in 2021. It is a medical emergency
1
Publisher’s Note: AccScience defined by the sudden reduction of blood flow to the brain, leading to cell death and
Publishing remains neutral with functional impairments. According to the World Health Organization, stroke accounts
regard to jurisdictional claims in 2
published maps and institutional for nearly 11% of global deaths. Furthermore, global DALYs due to stroke have surged
affiliations. from 119.89 million in 1990 to 159.86 million in 2021, driven by population growth and

Volume 2 Issue 4 (2025) 1 doi: 10.36922/AIH025140025