The Potential of Digital Twins in Stroke Care: A Systematic Review of Current Applications and Future Perspectives

Background: Digital twin technology holds promise for personalized stroke care, but current applications remain fragmented. This systematic review investigates how digital twins are currently utilized in the stroke care continuum. Methods: Following PRISMA guidelines, we conducted a systematic search of PubMed, Web of Science, and the Cochrane Library through April 2025. Studies applying digital twins to acute ischemic stroke care were included. Each study was categorized along the stroke care continuum (pre-stroke, in-hospital, post-stroke) and assessed using a digital twin maturity framework (L0 to L3). We extracted data on clinical intent, modeling approach, validation strategy, study design, population, sample size, and key outcomes to enable structured synthesis. Results: Eight studies met inclusion criteria. Half targeted pre-stroke risk prediction (e.g., modeling atherosclerosis or atrial fibrillation), 2 simulated mechanical thrombectomy, 1 supported prehospital diagnosis, and 1 predicted post-stroke disease progression. Most models remained at maturity levels L1 to L2, lacking real-time updating or workflow integration. Technologies included machine learning (n = 3), computational fluid dynamics (n = 3), and hybrid or rule-based approaches (n = 2). Conclusions: Digital twins in stroke care are promising but remain preclinical. Current models predominantly address pre-stroke risk prediction or procedural simulation, with limited representation of acute decision support or post-stroke monitoring. Clinical integration is constrained by low technological maturity, limited real-world validation, and a lack of interoperability.