Publication details

Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke

Authors	CIMFLOVÁ Petra GOLAN Rotem OSPEL Johanna M SOJOUDI Alireza DUSZYNSKI Chris ELEBUTE Ibukun EL-HARIRI Houssam MOUSAVI Seyed Hossein NETO Luis A Souto Maior PINKY Najratun BELAND Benjamin BALA Fouzi KASHANI Nima R HU William JOSHI Manish QIU Wu MENON Bijoy K
Year of publication	2022
Type	Article in Periodical
Magazine / Source	NEURORADIOLOGY
MU Faculty or unit	Faculty of Medicine
Citation
Web	https://link.springer.com/article/10.1007/s00234-022-02978-x
Doi	http://dx.doi.org/10.1007/s00234-022-02978-x
Keywords	Machine learning; Large vessel occlusion; Stroke; Automatic detection
Description	Purpose CT angiography (CTA) is the imaging standard for large vessel occlusion (LVO) detection in patients with acute ischemic stroke. StrokeSENS LVO is an automated tool that utilizes a machine learning algorithm to identify anterior large vessel occlusions (LVO) on CTA. The aim of this study was to test the algorithm's performance in LVO detection in an independent dataset. Methods A total of 400 studies (217 LVO, 183 other/no occlusion) read by expert consensus were used for retrospective analysis. The LVO was defined as intracranial internal carotid artery (ICA) occlusion and M1 middle cerebral artery (MCA) occlusion. Software performance in detecting anterior LVO was evaluated using receiver operator characteristics (ROC) analysis, reporting area under the curve (AUC), sensitivity, and specificity. Subgroup analyses were performed to evaluate if performance in detecting LVO differed by subgroups, namely M1 MCA and ICA occlusion sites, and in data stratified by patient age, sex, and CTA acquisition characteristics (slice thickness, kilovoltage tube peak, and scanner manufacturer). Results AUC, sensitivity, and specificity overall were as follows: 0.939, 0.894, and 0.874, respectively, in the full cohort; 0.927, 0.857, and 0.874, respectively, in the ICA occlusion cohort; 0.945, 0.914, and 0.874, respectively, in the M1 MCA occlusion cohort. Performance did not differ significantly by patient age, sex, or CTA acquisition characteristics. Conclusion The StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.