Multicenter validation of artificial intelligence software predicting large vessel occlusion using noncontrast brain CT

BackgroundTo validate JLK-CTL, an artificial intelligence (AI) software developed to predict large vessel occlusion (LVO) using non-contrast CT (NCCT) scans, and to investigate its clinical implications regarding both infarct volume and functional outcomes. MethodsBetween January-2021 and April-2023, a consecutive series of patients who concurrently underwent CT angiography and NCCT within 24 hours of last- known-well (LKW) were collected. LVO was confirmed through consensus among three experts reviewing CT angiography. Infarct volumes were quantified using diffusion-weighted imaging (DWI) conducted within seven days of the NCCT. The performance of the JLK-CTL was evaluated based on the area under the receiver operating characteristic curve (AUROC), as well as its sensitivity and specificity. The association of JLK-CTL LVO scores with infarct volumes and functional outcomes was assessed using Pearson correlation and logistic regression analyses, respectively. ResultsOf 1,391 screened patients, 774 (mean age 69.0 {+/-} 13.6 years, 57.6% men) were included. The median time from LKW to NCCT was 3.1 hours (IQR 1.5-7.4), with 24.2% (n=187) presenting LVO. The JLK-CTL demonstrated AUROC of 0.832 (95% CI 0.804-0.858), with a sensitivity of 0.711 (95% CI 0.641-0.775) and a specificity of 0.830 (95% CI 0.797-0.859) at the predefined threshold. Incorporating the National Institute of Health Stroke Scale into the model increased the AUROC to 0.872 (95% CI 0.846-0.894; p<0.001). The LVO scores showed a significant correlation with infarct volumes on follow-up DWI (r=0.53; p<0.001). When JLK-CTL LVO scores were categorized based on observed frequency of LVO, the highest JLK-CTL LVO scores (51-100) group showed an independent association with unfavorable functional outcomes (adjusted odds ratio 9.48; 95% CI 3.98-22.55). ConclusionThe performance of the AI software in predicting LVO was validated across multiple centers. This tool has the potential to assist physicians in optimizing stroke management workflows, especially in resource-limited settings.