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

Cimflova, Petra; Golan, Rotem; Ospel, Johanna M.; Sojoudi, Alireza; Duszynski, Chris; Elebute, Ibukun; El-Hariri, Houssam; Hossein Mousavi, Seyed; Neto, Luis A. Souto Maior; Pinky, Najratun; Beland, Benjamin; Bala, Fouzi; Kashani, Nima R.; Hu, William; Joshi, Manish; Qiu, Wu; Menon, Bijoy K.

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.

VASCULAR disease diagnosis; COMPUTER software; STROKE; CAROTID artery diseases; MACHINE learning; RETROSPECTIVE studies; AUTOMATION; COMPUTED tomography; RECEIVER operating characteristic curves; SENSITIVITY & specificity (Statistics); ACUTE diseases; ALGORITHMS

Neuroradiology, 2022, Vol 64, Issue 12, p2245

0028-3940

Academic Journal

10.1007/s00234-022-02978-x