Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke

作者：Hang Qu, Hui Tang, Dongyang Gao, Yongxin Li, Yi Zhao, Qiqi Ban, Yu‐Chen Chen, Lu Lu, Wei Wang · 发表于：Frontiers in Neurology · 年份：2024 · DOI：10.3389/fneur.2024.1477811 · 被引用次数：3 · 研究领域：Acute Ischemic Stroke Management、Cerebrovascular and Carotid Artery Diseases、Advanced X-ray and CT Imaging

Purpose Rapid diagnosis of acute ischemic stroke (AIS) is critical to achieve positive outcomes and prognosis. This study aimed to construct a model to automatically identify the infarct core based on non-contrast-enhanced CT images, especially for small infarcts. Methods The baseline CT scans of AIS patients, who had DWI scans obtained within less than 2 h apart, were included in this retrospective study. A modified Target-based deep learning model of YOLOv5 was developed to detect infarctions on CT. Randomly selected CT images were used for testing and evaluated by neuroradiologists and the model, using the DWI as a reference standard. Intraclass correlation coefficient (ICC) and weighted kappa were calculated to assess the agreement. The paired chi-square test was used to compare the diagnostic efficacy of physician groups and automated models in subregions. p < 0.05 was considered statistically significant. Results Five hundred and eighty four AIS patients were enrolled in total, finally 275 cases were eligible. Modified YOLOv5 perform better with increased precision (0.82), recall (0.81) and mean average precision (0.79) than original YOLOv5. Model showed higher consistency to the DWI-ASPECTS scores (ICC = 0.669, κ = 0.447) than neuroradiologists (ICC = 0.452, κ = 0.247). The sensitivity (75.86% vs. 63.79%), specificity (98.87% vs. 95.02%), and accuracy (96.20% vs. 91.40%) were better than neuroradiologists. Automatic model had better diagnostic efficacy than phys...