Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Area of Science:

• Neurology
• Medical Imaging
• Artificial Intelligence

Background:

• Aging populations increase the burden of chronic diseases, particularly cerebrovascular conditions like stroke.
• Stroke, a critical cerebrovascular event, necessitates rapid and accurate management to minimize brain tissue loss.

Purpose of the Study:

• To explore the application of artificial intelligence (AI) methods in stroke imaging and management.
• To highlight the potential of both classical machine learning and deep learning models in various stroke-related tasks.

Main Methods:

• Review of AI techniques including support vector machines, random forests, logistic regression, linear discriminant analysis, convolutional neural networks, recurrent neural networks, autoencoders, and U-Net.
• Application of AI to stroke management tasks: time-to-event determination, stroke confirmation, large vessel occlusion detection, diffusion/perfusion assessment, core/penumbra identification, segmentation, and outcome prediction.

Main Results:

• AI methods demonstrate broad applicability across diverse aspects of stroke management.
• Potential for AI to enhance speed, accuracy, and reliability in clinical decision-making for stroke.

Conclusions:

• AI holds significant promise for improving stroke care, from diagnosis to prognosis.
• Emphasizes the critical need for unbiased, generalizable, explainable, and trustworthy AI models, built on diverse data, for successful clinical integration.