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. 2022 Aug;14(8):794-798.
doi: 10.1136/neurintsurg-2021-017842. Epub 2021 Aug 19.

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

Sven P R Luijten #  1 , Lennard Wolff #  2 , Martijne H C Duvekot  3   4 , Pieter-Jan van Doormaal  2 , Walid Moudrous  5 , Henk Kerkhoff  4 , Geert J Lycklama A Nijeholt  6 , Reinoud P H Bokkers  7 , Lonneke S F Yo  8 , Jeannette Hofmeijer  9 , Wim H van Zwam  10 , Adriaan C G M van Es  11 , Diederik W J Dippel  3 , Bob Roozenbeek  2   3 , Aad van der Lugt  2 ; MR CLEAN Registry and PRESTO investigators
Collaborators, Affiliations

Diagnostic performance of an algorithm for automated large vessel occlusion detection on CT angiography

Sven P R Luijten et al. J Neurointerv Surg. 2022 Aug.

Abstract

Background: Machine learning algorithms hold the 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 CT angiography (CTA).

Methods: 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 (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC).

Results: We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a 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.

Conclusion: The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement.

Keywords: CT Angiography; stroke; thrombectomy.

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Conflict of interest statement

Competing interests: WHvZ reports grants from Stryker and Cerenovus, all paid to the institution. DWJD reports funding from the Dutch Heart Foundation, Brain Foundation Netherlands, The Netherlands Organisation for Health Research and Development, Health Holland Top Sector Life Sciences and Health, and unrestricted grants from Penumbra, Stryker, Medtronic, Thrombolytic Science, LLC, and Cerenovus, all paid to the institution. AvdL reports grants from Penumbra, Stryker, Cerenovus, and Medtronic, all paid to the institution.

Figures

Figure 1
Figure 1
Algorithm output of three patients showing maximum intensity projection reconstructions indicating the location of the occlusion by a blue occlusion box in axial, coronal and sagittal views. (A) Patient with a left internal carotid artery terminus occlusion. (B) Patient with a left proximal M1 occlusion. (C) Patient with a right short segment M2 occlusion. Contrast opacification is seen just distal of the occlusion site indicating good collateral flow.
Figure 2
Figure 2
Algorithm output of two patients showing maximum intensity projection reconstructions with incorrect placement of the blue occlusion box in axial, coronal and sagittal view. (A) Patient with a left M2 occlusion. The occlusion box is incorrectly placed more inferior in the temporal lobe. (B) Patient with a left intracranial internal carotid artery occlusion resulting in the reduced vessel density seen in the left hemisphere. The occlusion box was incorrectly placed in the right hemisphere on the middle cerebral artery bifurcation.
See this image and copyright information in PMC

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