Artificial Intelligence algorithms for decision-making in thrombolysis and thrombectomy

Abstract

Acute ischemic stroke is a medical emergency in which every minute of delay results in irreversible loss of brain tissue. The main treatment modalities—intravenous thrombolysis and endovascular thrombectomy—have strict time windows and depend critically on the accuracy of neuroimaging. Conventional image interpretation requires substantial clinical expertise, is time-consuming, and is subject to interobserver variability. Modern artificial intelligence (AI) algorithms open new opportunities for the automated detection of vascular occlusions, assessment of ischemic core volume, and generation of real-time treatment recommendations. The application of these algorithms can significantly reduce the time from patient admission to the initiation of reperfusion therapy, improve the accuracy of patient selection, and standardize clinical decision-making.

Objective: To summarize current evidence on the role of AI algorithms in decision-making for thrombolysis and thrombectomy and to assess their potential to improve the speed and accuracy of patient selection.

Materials and methods: A literature review (2015–2025) was conducted using the PubMed, Scopus, Web of Science, and Google Scholar databases with the keywords “artificial intelligence,” “machine learning,” “deep learning,” “stroke,” “thrombolysis,” and “thrombectomy” to synthesize contemporary data on the use of AI algorithms in clinical decision-making for acute ischemic stroke. Clinical studies, reviews, and protocols describing the application of AI in neuroimaging, prognostication, and patient stratification were analyzed.

Results: Deep learning algorithms (e.g., Viz.ai, e-ASPECTS) enable automated processing of computed tomography and magnetic resonance imaging, rapidly identifying ischemic lesions and vascular occlusions. This reduces the time from diagnosis to treatment by 15–37 minutes, improves the reproducibility of assessments, and optimizes patient selection for reperfusion therapy. Models integrating clinical and neuroimaging data demonstrate superior predictive accuracy and allow consideration of individual patient characteristics.

Conclusions: Artificial intelligence is becoming an integral tool in stroke management by providing rapid, standardized, and objective data analysis. Its implementation reduces “door-to-needle” and “door-to-puncture” times, improves treatment outcomes, and decreases disability. The synergy between clinicians and AI heralds a new era of personalized stroke therapy aimed at preserving brain tissue and saving patients’ lives.