Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review

BACKGROUND: Optimal outcomes after large-vessel occlusion (LVO) stroke are highly dependent on prompt diagnosis, effective communication, and treatment, making LVO an attractive avenue for the application of artificial intelligence (AI), specifically machine learning (ML). Our objective is to conduct a systematic review to describe existing AI applications for LVO strokes, delineate its effectiveness, and identify areas for future AI applications in stroke treatment and prognostication. METHODS: A systematic review was conducted by searching the PubMed, Embase, and Scopus databases. After deduplication, studies were screened by title and abstract. Full-text studies were screened for final inclusion based on prespecified inclusion and exclusion criteria. Relevant data were extracted from each study. RESULTS: Of 11,512 resultant articles, 40 were included. Of 30 studies with reported ML algorithms, the most commonly used ML algorithms were convolutional neural networks in 10 (33.3%), support vector machines in 10 (33.0%), and random forests in 9 (30.0%). Studies examining triage favored direct transport to a stroke center and predicted improved outcomes. ML techniques proved vastly accurate in identifying LVO on computed tomography. Applications of AI to patient selection for thrombectomy are lacking, although some studies determine individual patient eligibility for endovascular treatment with high accuracy. ML algorithms have reasonable accuracy in predicting clinical and angiographic outcomes and associated factors. CONCLUSIONS: AI has shown promise in the diagnosis and triage of patients with acute stroke. However, the role of AI in the management and prognostication remains limited and warrants further research to help in decision support.