Viz LVO versus Rapid LVO in detection of large vessel occlusion on CT angiography for acute stroke

A Comparison of CT Perfusion Output of RapidAI and Viz.ai Software in the Evaluation of Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Automated CTP postprocessing packages have been developed for managing acute ischemic stroke. These packages use image processing techniques to identify the ischemic core and penumbra. This study aimed to investigate the agreement of decision-making rules and output derived from RapidAI and Viz.ai software packages in early and late time windows and to identify predictors of inadequate quality CTP studies.

MATERIALS AND METHODS

One hundred twenty-nine patients with acute ischemic stroke who had CTP performed on presentation were analyzed by RapidAI and Viz.ai. Volumetric outputs were compared between packages by performing Spearman rank-order correlation and Wilcoxon signed-rank tests with subanalysis performed at early (<6 hours) and extended (>6 hours) time windows. The concordance of selecting patients on the basis of DAWN and DEFUSE 3 eligibility criteria was assessed using the McNemar test.

RESULTS

One hundred eight of 129 patients were found to have adequate-quality studies. Spearman rank-order correlation coefficients were calculated on time-to-maximum >6-second volume, time-to-maximum >10-second volume, CBF <30% volume, mismatch volume, and mismatch ratio between both software packages with correlation coefficients of 0.82, 0.65, 0.77, 0.78, 0.59, respectively. The Wilcoxon signed-rank test was also performed on time-to-maximum >6-second volume, time-to-maximum >10-second volume, CBF <30% volume, mismatch volume, and mismatch ratio with P values of .30, .016, <.001, .03, <.001, respectively. In a 1-sided test, CBF <30% was greater in Viz.ai (P < .001). Although this finding resulted in statistically significant differences, it did not cause clinically significant differences when applied to the DAWN and DEFUSE 3 criteria. A lower ejection fraction predicted an inadequate study in both software packages (P = .018; 95% CI, 0.01-0.113) and (P = .024; 95% CI, 0.008-0.109) for RapidAI and Viz.ai, respectively.

CONCLUSIONS

Penumbra and infarct core predictions between Rapid and Viz.ai correlated but were statistically different and resulted in equivalent triage using DAWN and DEFUSE3 criteria. Viz.ai predicted higher ischemic core volumes than RapidAI. Viz.ai predicted lower combined core and penumbra values than RapidAI at lower volumes and higher estimates than RapidAI at higher volumes. Clinicians should be cautious when using different software packages for clinical decision-making.

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.