Enhanced physician performance when using an artificial intelligence model to detect ischemic stroke on computed tomography

Acute ischemic stroke can be subtle to detect on non-contrast computed tomography imaging. We show that a novel artificial intelligence model significantly improves the performance of physicians, including ED physicians, neurologists and radiologists, in identifying and quantifying the volume of acute ischemic stroke lesions. This model may lead to improved clinical decision-making for stroke patients. ### Competing Interest Statement This study was funded by GE Healthcare. JMH, BCB, RG, CPB, JKC, BH, SM, JC, SD, WAH, RWR, AS, ABS, JDS, MDS, KJD, MHL, RGG were employees of Mass General Brigham and/or Massachusetts General Hospital at the time of this study, which had received institutional funding from GE Healthcare for the study. TZ, BX, JFK were employees of GE Healthcare at the time of this study. RWR reports the following competing interests: Rapid Medical (Clinical Trial DSMB); Microvention (Site PI); Penumbra (Site PI); National Institute of Neurological Disorders and Stroke (Research Grant); Society of Vascular and Interventional Neurology (Research Grant); Heitman Foundation (Research Grant). ### Funding Statement This study was funded by GE Healthcare. ### 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: This study was approved by the Mass General Brigham Institutional Review Board with waiver of informed consent per the Common Rule. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data generated for this study contains protected patient information. Some data may be available for research purposes from the corresponding author upon reasonable request.