NYMC Faculty Publications

The Impact of Artificial Intelligence on Large Vessel Occlusion Stroke Detection and Management: A Systematic Review Meta-Analysis

Author Type(s)

Student, Faculty

DOI

10.1016/j.ibmed.2024.100161

Journal Title

Intelligence Based Medicine

Document Type

Article

Publication Date

1-1-2024

Department

Neurology

Second Department

Medicine

Keywords

Acute ischemic stroke, Artificial intelligence, Augmented intelligence, Deep learning, Large vessel occlusion, Operations management, Thrombectomy

Disciplines

Medicine and Health Sciences

Abstract

Introduction: Stroke remains the second leading cause of death worldwide, with many survivors facing significant disabilities. In acute stroke care, the timeless adage 'Time is brain' underscores the vital need for quick action. Innovative Artificial Intelligence (AI) technology potentially enables swift detection and management of acute ischemic strokes, revolutionizing acute stroke care towards enhanced automation. Methods: The study is registered with Prospero under CRD42024496716 and adheres to the Problem, Intervention, Comparison, and Outcomes framework (PICO). The analysis used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Cochrane database, IEEE, Web of Science, ArXiv, MedRxiv, and Semantic Scholar. The articles included were published between 2019 and 2023. Out of 1528 articles identified, thirty-seven met the inclusion criteria. Results: We compared AI-augmented Large Vessel Occlusion (LVO) detection and non-AI LVO detection in various patient processing times related to emergent endovascular therapy in acute ischemic strokes. Triage Time, Door-to-Intervention Notification Time (INR), and Door-to -Arterial Puncture Time revealed an odds ratio (OR) of 0.39 (95 % CI: 0.29–0.54, p < 0.001), 0.30 (95 % CI: 0.21–0.42, p < 0.001), and 0.50 (95 % CI: 0detection 0.30–0.82, p = 0.007), respectively – all of which had negligible heterogeneity (I^2 = 0). CT-to-Puncture-Time and Door-to-CTA-Time yielded an OR of 0.57 (95 % CI: 0.31–1.04, p = 0.065) and 0.77 (95 % CI: 0.37–1.60, p = 0.489), respectively – both of which had negligible heterogeneity (I^2 = 0). The Last Known Well (LWK) to Time of Arrival resulted in an OR of 1.15 (95 % CI: 0.83–1.59, p = 0.409, I^2 = 0). AI stroke detection sensitivity OR of 0.91 (95 % CI: 0.88–0.95, p < 0.001) should be interpreted with potential heterogeneity in mind (I^2 = 69.3). National Institute of Health score (NIHSS) mean of 16.20 (95 % CI: 14.96–17.45, p = 0.001, I^2 = 0). Patient Transfer-Times between primary and comprehensive stroke centers generated an OR of 0.98 (95 % CI: 0.73–1.32, p = 893, I^2 = 0). Similarly, Door-in-Door-Out Time (DIDO) had an OR of 1.19 (95 % CI: 0.21–6.88, p = 0.848) and low heterogeneity (I^2 = 5.1). The results indicated significant differences across several parameters between the AI augmentation and non-AI groups. Conclusion: Our findings highlight how AI augments healthcare providers' ability to detect and manage strokes swiftly and accurately within acute care settings. As these technologies progress, healthcare organizations mature, and AI becomes more integrated into healthcare systems, longitudinal studies are critical in evaluating its impact on workflow efficiency, cost-effectiveness, and clinical outcomes.

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