Automated Emergent Large Vessel Occlusion Detection by Viz.ai Software and its Impact on Stroke Workflow Metrics in Stroke Centers: A Systematic Review and Meta-analysis
Khalid Sarhan1, Ibrahim Serag1, Ahmed Azzam2, Abdallah Abbas3, Ahmed E. Sarhan4, Ahmed Negida5
1Faculty of Medicine, Mansoura University, Mansoura, Egypt, 2Faculty of Medicine, October 6 University, Giza, Egypt, 3Faculty of Medicine, Al-Azhar University, Damietta, Egypt, 4Lecturer of Neurology, Al-Azhar University, Cairo, Egypt, 5Virginia Commonwealth University
Objective:
The aim of this systematic review and meta-analysis is to evaluate the impact of Viz.ai on stroke workflow efficiency in hospitals.
Background:
Stroke is a leading cause of disability and death worldwide, with timely diagnosis and intervention being critical for improving patient outcomes. The implementation of artificial intelligence (AI) particularly Viz.ai software in stroke care, has emerged as a promising tool to enhance the detection of large vessel occlusion (LVO), and to improve the stroke workflow metrics in hospitals.
Design/Methods:
Following the PRISMA guidelines, we conducted a comprehensive search on electronic databases including PubMed, Web of Science and Scopus databases to obtain relevant studies until 25/9/2024. Our outcomes were door to groin puncture (DTG) time, CT scan to start of endovascular treatment (EVT) time, CT scan to recanalization time, and door-in door-out time.
Results:
A total of 11 studies involving 15402 patients were included in our analysis. The pooled analysis demonstrated that the implementation of the AI algorithm was associated with lesser DTG time (SMD -0.47, 95% CI [-0.61, -0.32], P < 0.001) and CT scan to EVT time (SMD -0.65, 95% CI [-0.86, -0.43], P < 0.001), as well as CT to recanalization time (SMD -0.57, 95% CI [-0.74, -0.41], P < 0.001). Additionally, patients in the post-AI group had significantly lower door-in door-out time than pre-AI group (SMD -0.49, 95% CI [-0.71, -0.28], P < 0.001). No heterogeneity was found in all outcomes.
Conclusions:
Our results suggest that the integration of Viz.ai platform in stroke care, holds significant potential for reducing EVT delays in patients with LVO, and optimizing stroke flow metrics in comprehensive stroke centers. Further studies are required to validate its efficacy in improving clinical outcomes in patients with LVO.
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