on behalf of the MR CLEAN Registry and PRESTO investigators
Machine learning algorithms hold potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on computed tomography angiography (CTA).
Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (ICA/ICA-T, M1 or M2 occlusion). Impact of scan acquisition parameters on algorithm performance was also evaluated. Assessments done by neuroradiologists were used as reference. Diagnostic performance was assessed by means of sensitivity, specificity and area under the curve (AUC).
We analyzed CTA’s of 1,110 patients with LVO from the MR CLEAN Registry and of 625 patients with suspected acute ischemic stroke in PRESTO (141 with and 484 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO. Slice thickness ≥2 mm reduced the AUC from 0.83 (<1mm) to 0.71 (P<0.01) and venous scan phase reduced the AUC from 0.87 (equilibrium phase) to 0.74 (P<0.01).
The algorithm provided a high detection rate for proximal LVO, but performance varied by occlusion location and image quality.
trialregister.nl; NL7387