Evaluating the diagnostic accuracy of AI in ischemic and hemorrhagic stroke: A comprehensive meta-analysis

Stroke poses a significant health challenge, with ischemic and hemorrhagic subtypes requiring timely and accurate diagnosis for effective management. Traditional imaging techniques like CT have limitations, particularly in early ischemic stroke detection. Recent advancements in artificial intelligence (AI) offer potential improvements in stroke diagnosis by enhancing imaging interpretation. This meta-analysis aims to evaluate the diagnostic accuracy of AI systems compared to human experts in detecting ischemic and hemorrhagic strokes. The review was conducted following PRISMA-DTA guidelines. Studies included stroke patients evaluated in emergency settings using AI-Based models on CT or MRI imaging, with human radiologists as the reference standard. Databases searched were MEDLINE, Scopus, and Cochrane Central, up to January 1, 2024. The primary outcome measured was diagnostic accuracy, including sensitivity, specificity, and AUROC and the methodological quality was assessed using QUADAS-2. Nine studies met the inclusion criteria and were included. The pooled analysis for ischemic stroke revealed a mean sensitivity of 86.9% (95% CI: 69.9%–95%) and specificity of 88.6% (95% CI: 77.8%–94.5%). For hemorrhagic stroke, the pooled sensitivity and specificity were 90.6% (95% CI: 86.2%–93.6%) and 93.9% (95% CI: 87.6%–97.2%), respectively. The diagnostic odds ratios indicated strong diagnostic efficacy, particularly for hemorrhagic stroke (DOR: 148.8, 95% CI: 79.9–277.2). AI-Based systems exhibit high diagnostic accuracy for both ischemic and hemorrhagic strokes, closely approaching that of human radiologists. These findings underscore the potential of AI to improve diagnostic precision and expedite clinical decision-making in acute stroke settings.