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.
Kalyanpur et al. (Thu,) studied this question.