(VISIION-S): Viz.ai Implementation of Stroke augmented Intelligence and communications platform to improve Indicators and Outcomes for a comprehensive stroke center and Network - Sustainability

Early Experience With Artificial Intelligence Software to Detect Intracranial Occlusive Stroke in Trauma Patients

Objective Identifying ischemic stroke is a diagnostic challenge in the trauma subpopulation. We describe our early experience with artificial intelligence-assisted image analysis software for automatically identifying acute ischemic stroke in trauma patients.  Methods Patients were retrospectively screened for (i) admission to the trauma service at a level one trauma center between 2020 and 2022, (ii) radiologist-confirmed intracranial occlusion, (iii) occlusion identified on computed tomography angiography performed within 24 hours of admission, (iv) no intracranial hemorrhage, and (v) contemporaneous analysis with the large vessel occlusion (LVO) detection program. Baseline characteristics, stroke detection, response-activation, and outcome data were summarized.  Results Of 9893 trauma patients admitted, 88 (0.89%) patients had a cerebral stroke diagnosis, of which 10 patients (10/88; 11.4%) met inclusion criteria. Most patients were admitted following a fall (8/10; 80%). Six (6/10; 60.0%) patients had LVOs. The program correctly detected 83.3% (5/6) of patients, and these patients were triaged in less than one hour from arrival on average. The program did not falsely identify non-LVOs as LVOs for any patients. Conclusions Identifying adjunct tools to aid timely identification and treatment of ischemic stroke in trauma patients is necessary to increase the chances for meaningful neurological recovery. Our early experience exhibited potential for using automated software to aid occlusion identification and subsequent stroke team mobilization. Future studies in larger cohorts will expand upon these preliminary findings to establish the accuracy and clinical benefit of automated stroke detection tool integration for the trauma population.

The Influence of the Novel Computer-Aided Triage System Based on Artificial Intelligence on Endovascular Therapy in Patients with Large Vascular Occlusions: A Meta-Analysis

OBJECTIVE

The integration of artificial intelligence (AI) with modern healthcare has become increasingly prominent. The purpose of this study is to explore the impact of the novel computer-aided triage system based on artificial intelligence (AI-CTS) on endovascular therapy (EVT) in patients with large vascular occlusions (LVO). This study marks the first comprehensive systematic review and meta-analysis on the subject.

METHODS

A comprehensive study was performed on PubMed, Medline, Embase, Cochrane Library, and Chinese databases from their establishment to September 2023, in accordance with PRISMA recommendations. RevMan 5.4 software was used for summative analysis. The outcomes included door-to-groin (DTG) time, time from CT scan initiation to EVT, time from CT scan to reperfusion, and 90-day modified Rankin Scale (mRS).

RESULTS

A total of 7 studies involving 752 participants were included in the meta-analysis. The pooled results demonstrated that patients in the post-AI group had less time of DTG [SMD, 0.54; 95% CI, 0.40-0.69; P < 0.00001] and CT scan to EVT [SMD, 0.57; 95% CI, 0.42-0.73; P < 0.00001], as well as less time of CTA to recanalization [SMD, 0.63; 95% CI, 0.36-0.90; P < 0.00001]. There was no significant difference between the 2 groups in terms of the mRS at 90 days [OR, 0.66; 95% CI, 0.43-1.01; P = 0.06].

CONCLUSIONS

The combination of AI-CTS and EVT has improved the therapy process for LVO patients. However, the improvement in mRS at 90 days was not significant; further research is warranted.