Automated detection of large vessel occlusion: a multicenter study validating efficacy and proving clinical implications

Objective We aimed to validate a software, JLK-LVO, that automatically detects large vessel occlusion (LVO) on computed tomography angiography (CTA) using deep learning, within a prospective multicenter dataset. In addition, we calibrated the predicted probability of LVO against observed frequency and assessed the clinical implications of LVO probability in terms of follow-up infarct volume and functional outcome. Method From 2021 to 2023, we prospectively collected data from patients who underwent CTA within 24 hours of symptom onset at six university hospitals in Korea. The diagnostic performance of the software was evaluated using the area under the curve (AUC), sensitivity, and specificity across the entire study population and specifically in patients with isolated middle cerebral artery (MCA)-M2 occlusion. In addition, we compared LVO probabilities after stratifying patient into acute LVO, chronic LVO, isolated MCA-M2 occlusion, relevant MCA stenosis, and without steno-occlusion of MCA groups. We calibrated LVO probabilities in two ways: through mathematical calibration using logistic regression, and by refining LVO probabilities based on the observed frequency of LVO. We then assessed the association of LVO probability categories with infarct volume on follow-up diffusion-weighted imaging (DWI) and modified Rankin Scale (mRS) scores three months post-stroke, using ANOVA and the Cochran–Armitage test. Results After excluding 168 patients, 796 remained; the mean (SD) age was 68.9 (13.7) years, and 57.7% were men. LVO was present in 193 (24.3%) of these patients, and the median interval from last known well to CTA was 5.7 hours (IQR 2.5 to 12.1 hours). At default threshold of 0.5, the software achieved an AUC of 0.944 (95% CI 0.926–0.960), with a sensitivity of 0.896 (0.845–0.936) and a specificity of 0.904 (0.877–0.926). In isolated MCA-M2 occlusion, the AUROC was 0.880 (95% CI 0.824–0.921). Compared to the without steno-occlusion of MCA groups (median LVO probability 0.5, interquartile range 0.1 – 6.5), relevant stenosis (median 15.3, 2.4 –77.4) and isolated MCA-M2 occlusion (82.1, 40.9 – 98.2) groups had significantly higher LVO probability. Due to sparse data between 20-60% of LVO probabilities, recategorization into unlikely (0-20% LVO scores), less likely (20-60%), possible (60-90%), and suggestive (90-100%) provided a reliable estimation of LVO compared with mathematical calibration. The category of LVO probabilities was significantly associated with follow-up infarct volumes on DWI and 3-month mRS scores. Conclusion In this multicenter validation study, we proved the clinical efficacy of the software in detecting LVO on CTA. Additionally, using large-scale real-world data, we calibrated the LVO probabilities, which may provide a more confident estimation of LVO for practicing physicians. ### Competing Interest Statement Sueyoung Ha, Myungjae Lee, Dong-Min Kim, and Wi-Sun Ryu are employess of JLK Inc. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All patients or their legal representatives gave a written informed consent. The study protocol was approved by institutional review board of Seoul National University Bundang Hospital [B-2307-841-303]. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.