The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) patients. Computed tomographic perfusion (CTP) can be used to evaluate the ischemic core and deficit, covering most of the territories of anterior circulation, but many community hospitals and primary stroke centers do not have the capability to perform CTP scan in emergency situation. This study aimed to identify AIS lesions from widely available non-contrast computed tomography (NCCT) and CT angiography (CTA) using deep learning. A total of 345AIS patients from our emergency department were included. A multi-scale 3D convolutional neural network (CNN) was used as the predictive model with inputs of NCCT, CTA, and CTA+ (8 s delay after CTA) images. An external cohort with 108 patients was included to further validate the generalization performance of the proposed model. Strong correlations with CTP-RAPID segmentations (r = 0.84 for core, r = 0.83 for deficit) were observed when NCCT, CTA, and CTA+ images were all used in the model. The diagnostic decisions according to DEFUSE3 showed high accuracy when using NCCT, CTA, and CTA+ (0.90±0.04), followed by the combination of NCCT and CTA (0.87±0.04), CTA-alone (0.76±0.06), and NCCT-alone (0.53±0.09).

准确识别不可逆梗死和可挽救组织对于规划急性缺血性卒中 (AIS) 患者的治疗非常重要。计算机断层扫描灌注（CTP）可用于评估缺血核心和缺损，覆盖大部分前循环区域，但许多社区医院和初级脑卒中中心没有能力在紧急情况下进行 CTP 扫描。本研究旨在使用深度学习从广泛可用的非对比计算机断层扫描 (NCCT) 和 CT 血管造影 (CTA) 中识别 AIS 病变。我们急诊科共纳入 345 名 AIS 患者。使用多尺度 3D 卷积神经网络 (CNN) 作为预测模型，输入 NCCT、CTA 和 CTA+（CTA 后延迟 8 秒）图像。纳入了一个包含 108 名患者的外部队列，以进一步验证所提出模型的泛化性能。与 CTP-RAPID 分割的强相关性（ 当模型中都使用 NCCT、CTA 和 CTA+ 图像时，观察到核心r  = 0.84，缺陷r = 0.83）。根据 DEFUSE3 的诊断决策在使用 NCCT、CTA 和 CTA+（0.90±0.04）时显示出较高的准确性，其次是 NCCT 和 CTA 的组合（0.87±0.04）、单独的 CTA（0.76±0.06）和单独的 NCCT (0.53±0.09)。