Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence–driven applications for acute stroke triage, surveillance, and prediction.
Prompt detection and treatment of acute cerebrovascular disease is critical to reduce morbidity and mortality. The current application of AI in this field has allowed for vast opportunities to improve treatment selection and clinical outcomes by aiding in all parts of the diagnostic and treatment pathway, including detection, triage, and outcome prediction. Future studies validating AI techniques are needed to allow for more widespread use in various practice environments.