Rapid detection of large vessel occlusion (LVO) is crucial for improving outcomes of acute ischemic stroke. This study provides a real-world, head-to-head comparison of two commercial AI tools for automated LVO detection—RAPID CTA (vessel density-based) and JLK LVO (deep learning-based)—in a Korean stroke center. We retrospectively analyzed 176 consecutive patients with suspected stroke who underwent both CT angiography and CT perfusion. The performance of RAPID CTA and JLK LVO was compared against expert neuroradiologist consensus using the area under the receiver operating characteristic curve (AUROC). Misclassified cases (false positives FPs and false negatives FNs) were reviewed to determine their underlying causes. LVO was confirmed in 53 of 176 patients (30.1%). Both tools demonstrated high and comparable overall performance (AUROC 0.93 for both, p = 0.64). The causes for misclassifications were also consistent across both platforms. The most common cause of FPs was high-grade intracranial stenosis mimicking occlusion. The primary cause for FNs was the presence of well-developed collateral flow in distal occlusions, which masks the vessel cut-off. However, a matched-sensitivity analysis revealed different performance trade-offs; at a predefined threshold yielding 83% sensitivity, JLK LVO demonstrated higher specificity than RAPID CTA (0.96 vs. 0.89). Both RAPID CTA and JLK LVO are effective tools, but they exhibit distinct performance trade-offs. A clear understanding of each tool’s common pitfalls and performance trade-offs is crucial for clinicians to effectively integrate these AI results for optimal patient care.
Ha et al. (Tue,) studied this question.