Abstract WP70: Artificial Intelligence Based Detection of Large Vessel Occlusion on Non-Contrast Computed Tomography in Stroke

Abstract

Introduction: Rapid detection of a large vessel occlusion (LVO) in acute ischemic stroke is beneficial as it may accelerate therapeutic management. The hyperdense artery sign (HAS) on non-contrast CT (NCCT) is result of an acute thrombolytic occlusion and is known as the earliest radiological marker to identify LVO. However, HAS is not apparent for all LVOs and detection by a less-trained eye can be sub-optimal. Artificial intelligence (AI) may support the non-trained radiologist in swift and accurate LVO detection. We evaluated the LVO detection accuracy of AI, and compared this to human expert performance. Materials and Methods: We used a convolutional neural network (CNN) developed by Nico.lab (www.nico-lab.com) that automatically detects and segments thrombi on NCCT using a patch-based approach. The CNN was trained using thrombus and non-thrombus patches, combined with the contralateral side. In a retrospective analysis of thin-slice NCCTs, obtained from Amsterdam UMC patients, with (ICA-T, M1, or M2) and without occlusions (stroke mimics), the CNN and two observers (>15y and >4y of experience) assessed LVO-presence and location. Ground truth was established by consensus between two different experts with >5y of experience, using both CTA and NCCT. Thrombus segmentations were considered accurate if segmentations overlapped with the ground truth. Sensitivity and specificity of the LVO detection were assessed. Results: We included 107 patients, of which 59 had proven LVOs. Nico.lab CNN showed a thrombus segmentation accuracy of 81%, vs. 81% and 77% of human experts. Sensitivity of LVO detection by CNN was 0.86, vs. 0.95 and 0.79 for human observers. Specificity was 0.65 for the CNN vs. 0.58 and 0.82 for human experts respectively. Conclusion: AI-based LVO detection on NCCT showed comparable results to expert observers. This supportive tool could lead to earlier detection of LVO in acute stroke situations. Fig 1A. HAS; B. CNN output (red); C. clot on CTA (blue arrow)

Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography

“…Vendors include ischemaView (RAPID CTA, is-chemaView inc., Menlo Park, CA, USA), Viz.ai (Viz LVO, Viz.ai, San Francisco, CA, USA), Nico.lab (Stroke Viewer, nico-lab, Amsterdam, The Netherlands) or Brainomix (e-CTA, Brainomix, Oxford, UK). Reported sensitivities for LVO detection range between 0.81 and 0.97, although data are not complete for all vendors [16][17][18][19][20].…”