A scoping review and staged research agenda for artificial intelligence in viscoelastic haemostatic assays. Download PDF Abstract Viscoelastic haemostatic assays (VHA), including thromboelastography and rotational thromboelastometry, capture dynamic whole-blood coagulation but are often interpreted through derived parameters and thresholds. We conducted a prospectively registered scoping review, aligned with PRISMA-ScR and JBI guidance, to map artificial intelligence (AI) and machine-learning applications involving VHA data.
Five databases were searched, 493 unique records were screened and 27 studies were included. Most studies used VHA-derived parameters as predictors; three used VHA to define outcomes or phenotypes, and one modelled raw device signals. Applications spanned trauma, perioperative and transplant medicine, vascular/cardiology, obstetrics, critical care and haemostatic diagnostics.
Reported discrimination was sometimes high, but evidence was limited by small cohorts, mixed-variable models, internal validation, abstract-only reports, limited calibration and scarce external testing. Current evidence supports minute-level acceleration, especially A5/A10-based inference. Three-to-five-minute raw-signal modelling is a plausible research target; sub-minute and 20-s prediction remain hypotheses.
Medical Disclaimer: This article is for informational purposes only and does not constitute medical advice. Always consult with qualified healthcare professionals for medical decisions and treatment options.
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