Segmentation of the Hyperdense Artery Sign on Noncontrast CT in Ischemic Stroke Using Artificial Intelligence
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Background and Purpose We developed and validated an automated hyperdense artery sign (HAS) segmentation algorithm for the distal internal carotid artery and middle cerebral artery on noncontrast brain computed tomography (NCCT) using a multicenter dataset with independent annotation performed by two experts. Methods For training and external validation, we included patients with ischemic stroke who underwent concurrent NCCT and CT angiography between May 2011 and December 2022 at six hospitals and one hospital, respectively. For clinical validation, nonoverlapping patients admitted within 24 hours of onset were consecutively included between December 2020 and April 2023 from six hospitals. The model was trained using the 2D U-Net deep-learning architecture with manual annotation by two experts. We constructed models trained on datasets annotated individually by each expert, and an ensemble model using shuffled annotations by both experts. The performance of the models was compared using the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results This study included 673, 365, and 774 patients in the training/internal validation, external validation, and clinical validation datasets, respectively, who were aged 68.8±13.2, 67.8±13.4, and 68.8±13.6 years (mean±standard deviation) and comprised 55.0%, 59.5%, and 57.6% males. The ensemble model achieved higher AUROC and sensitivity than the models trained on annotations by a single expert in the external validation. For the clinical validation dataset, the ensemble model exhibited an AUROC of 0.846 (95% confidence interval [CI], 0.819–0.871), sensitivity of 76.8% (95% CI, 65.1%–86.1%), and specificity of 88.5% (95% CI, 85.9%–90.8%). The predicted volume of the clot was correlated with the infarct volume in follow-up diffusion-weighted imaging (r=0.42, p<0.001). Conclusions Our new algorithm can rapidly and accurately identify the HAS, and so can facilitate the screening of potential patients requiring intervention.
Abstract
Background and Purpose We developed and validated an automated hyperdense artery sign (HAS) segmentation algorithm for the distal internal carotid artery and middle cerebral artery on noncontrast brain computed tomography (NCCT) using a multicenter dataset with independent annotation performed by two experts. Methods For training and external validation, we included patients with ischemic stroke who underwent concurrent NCCT and CT angiography between May 2011 and December 2022 at six hospitals and one hospital, respectively. For clinical validation, nonoverlapping patients admitted within 24 hours of onset were consecutively included between December 2020 and April 2023 from six hospitals. The model was trained using the 2D U-Net deep-learning architecture with manual annotation by two experts. We constructed models trained on datasets annotated individually by each expert, and an ensemble model using shuffled annotations by both experts. The performance of the models was compared using the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results This study included 673, 365, and 774 patients in the training/internal validation, external validation, and clinical validation datasets, respectively, who were aged 68.8±13.2, 67.8±13.4, and 68.8±13.6 years (mean±standard deviation) and comprised 55.0%, 59.5%, and 57.6% males. The ensemble model achieved higher AUROC and sensitivity than the models trained on annotations by a single expert in the external validation. For the clinical validation dataset, the ensemble model exhibited an AUROC of 0.846 (95% confidence interval [CI], 0.819–0.871), sensitivity of 76.8% (95% CI, 65.1%–86.1%), and specificity of 88.5% (95% CI, 85.9%–90.8%). The predicted volume of the clot was correlated with the infarct volume in follow-up diffusion-weighted imaging (r=0.42, p<0.001). Conclusions Our new algorithm can rapidly and accurately identify the HAS, and so can facilitate the screening of potential patients requiring intervention.