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Comparison of automated ASPECTS, large vessel occlusion detection and CTP analysis provided by Brainomix and RapidAI in patients with suspected ischaemic stroke. | LitMetric

AI Article Synopsis

  • The study investigates the correlation between outputs from two different CTP software packages, Brainomix and RapidAI, to assess patient eligibility for mechanical thrombectomy in cases of arterial occlusion.
  • Results showed a high correlation in measuring ischaemic core and penumbra volumes, with Brainomix performing better in predicting final infarct volume and detecting large vessel occlusion.
  • Despite the high correlation, the study found discrepancies in thrombectomy eligibility in 15% of cases, suggesting that clinical decisions should incorporate more than just imaging data.

Article Abstract

Objectives: The ischaemic core and penumbra volumes derived from CTP aid the selection of patients with an arterial occlusion for mechanical thrombectomy. Different post-processing software packages may give different CTP outputs, potentially causing variable patient selection for mechanical thrombectomy. The study aims were, firstly, to assess the correlation in CTP outputs from software packages provided by Brainomix and RapidAI. Secondly, the correlation between automated ASPECTS and neuroradiologist-derived ASPECTS and accuracy in detecting large vessel occlusion was assessed.

Materials And Methods: This retrospective study included patients undergoing CTP for suspected anterior circulation large vessel occlusion. Pearson's correlation coefficient was used for testing the correlation in CTP outputs, ASPECTS/automated ASPECTS, and-in those with complete or near complete occlusion-final infarct volume. Diagnostic statistics were calculated for large vessel occlusion detection.

Results: Correlation was high for ischaemic core and penumbra volumes (0.862 and 0.832, respectively) but lower for the mismatch ratio (0.477). Agreement in mechanical thrombectomy eligibility was achieved in 85% of cases (46/54). Correlation between ischaemic core and final infarct volume was higher for Brainomix (0.757) than for RapidAI (0.595). The correlation between ASPECTS and automated ASPECTS (0.738 and 0.659) and the accuracy of detecting large vessel occlusion (77% and 71%) was higher for Brainomix than for RapidAI.

Conclusion: There was high correlation between the CTP output from Brainomix and RapidAI. However, there was a difference in MT eligibility in 15% of cases, which highlights that the decision regarding MT should not be based on imaging parameters alone.

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Source
http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2022.106702DOI Listing

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