Automated detection of large vessel occlusion using deep learning: a pivotal multicenter clinical trial and reader assessment study

Background: This multicenter clinical trial evaluated the stand-alone efficacy and the improvements in diagnostic accuracy of early-career physicians using a deep learning-based software to detect large vessel occlusion (LVO) in CT angiography (CTA). Methods: This multicenter pivotal clinical trial included 595 ischemic stroke patients from January 2018 to September 2023. Standard reference and LVO locations (intracranial internal carotid artery [ICA], M1, or M2) were determined by consensus among three expert vascular neurologists after reviewing CTA, MR imaging, and symptom data. The performance of the JLK-LVO software was evaluated against a standard reference, and its impact on the diagnostic accuracy of four residents involved in stroke care was assessed. Performance metrics included the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Among the 595 patients (mean age 68.5, SD 13.4 years, 56% male), 275 (46.2%) had LVO. The median time interval from the last known well moment to the CTA was 46 hours (IQR 11.8 to 64.4). For LVO detection, the software demonstrated a sensitivity of 86% and a specificity of 97%. For isolated M2 occlusions, it achieved a sensitivity of 69% and a specificity of 96%. The reader assessment study showed that reading with software assistance improved the sensitivity by 4.0% and AUROC by 2.4% (all p < 0.001) compared to readings without AI assistance. Conclusion: The software demonstrated a high detection rate for proximal LVO and moderate sensitivity for isolated MCA-M2 occlusion. In addition, the software improved diagnostic accuracy of early-career physicians in detecting LVO. ### Competing Interest Statement Sue Young Ha, Hotak Hong, Dongmin Kim, Myung_Jae Lee, and Wi-Sun Ryu are emplyees of JLK Inc. ### Funding Statement This study was funded by JLK Inc. ### 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: The study protocol was approved by the institutional review board of Chonnam National University Hospital and Daejeon Eulji Hospital. 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