ArticleAJNR. American journal of neuroradiology2024
A Comparison of CT Perfusion Output of RapidAI and Viz.ai Software in the Evaluation of Acute Ischemic Stroke.
Article in AJNR. American journal of neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Understanding CT Perfusion in Acute Ischemic Stroke: How Algorithms Shape Perfusion Maps.Diagnostics (Basel, Switzerland) · 2026Review
- Generative Artificial Intelligence to Automate Cerebral Perfusion Mapping in Acute Ischemic Stroke from Non-contrast Head Computed Tomography Images: Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Transforming Stroke Diagnosis with Artificial Intelligence: A Scoping Review of Brainomix e-Stroke, Aidoc, RapidAI, and Viz.ai.Medicina (Kaunas, Lithuania) · 2026Article
- Comparison of CT-perfusion software packages and validation with true infarct core in patients with acute ischemic stroke.BMC medical imaging · 2025Article
- Evaluation of a Deep Learning Tool for Detecting Large Vessel Occlusion and Intracranial Hemorrhage on Noncontrast Computed Tomography Scans.Stroke (Hoboken, N.J.) · 2025Article
- Association of Computed Tomography Perfusion Parameters with 90-Day Functional Independence After Endovascular Thrombectomy.Journal of clinical medicine · 2025Article
- Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data.NPJ digital medicine · 2025Article
- Augmenting radiological assessment of imaging evident dementias with radiomic analysis.NPJ dementia · 2025Article
- Current Stroke Solutions Using Artificial Intelligence: A Review of the Literature.Brain sciences · 2024Review
- A clinical and computed tomography-based nomogram to predict the outcome in patients with anterior circulation large vessel occlusion after endovascular mechanical thrombectomy.Japanese journal of radiology · 2024Article
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Authors and funding
13 authors.
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Abstract
background and purposeAutomated 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
methodsOne 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.
resultsOne 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
conclusionsPenumbra 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.
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