Artificial Intelligence-Driven Detection of Large Vessel Occlusions on NCCT: A Multi-Institutional Study.

Background and purpose: Imaging triage of stroke patients is primarily based on perfusion imaging. Simplified triage based on non-contrast CT are limited (NCCT). To evaluate the predictive capability of a deep learning algorithm, "Triage Stroke" (Brainomix 360) in identifying anterior circulation large vessel occlusions (LVO) on NCCT in patients with suspected acute ischemic stroke (AIS).

Materials and methods: This multi-institutional study analyzed 612 patients with suspected AIS at 3 US comprehensive stroke centers. A balanced cohort of consecutive patients with and without anterior circulation LVO was analyzed. Ground truth was based on concurrent CTA evaluated by site neuroradiologists. The primary outcome was predictive performance for LVO detection. The secondary outcomes were 1) prospective comparison of NCCT LVO detection against general radiologists and subspecialty neuroradiologists, and 2) the influence of NIHSS on the model.

Results: Triage Stroke software detected an LVO on NCCT with a 67% sensitivity and 93% specificity. The positive and negative predictive values were 59% and 95%, respectively, with an area under the curve (AUC) of 0.8. The software's sensitivity for LVO detection was significantly higher than the group average of all radiologists (difference = 20.5%; CI, 8.26-32.78; P = .001) and was also higher when separated into general and neuroradiology subgroups. The AUC for NCCT LVO was significantly higher than the group of all readers (difference = 11%; CI, 4%-17%; P < .001), and the nonexpert readers (difference = 13%, CI, 7%-20%; P < .001). The addition of NIHSS to the model yielded a high specificity (99%) and similar sensitivity (65%), resulting in the optimum positive predictive value of all models tested (91%).

Conclusions: Triage Stroke software demonstrated strong predictive capabilities for NCCT detection of anterior circulation LVOs outperforming radiologists. Coupled with NIHSS it may simplify identification of endovascular candidates especially in resource-constrained environments worldwide.