Computed Tomography (CT) is commonly used to image acute ischemic stroke
(AIS) patients, but its interpretation by radiologists is time-consuming and
subject to inter-observer variability. Deep learning (DL) techniques can
provide automated CT brain scan assessment, but usually require annotated
images. Aiming to develop a DL method for AIS using labelled but not annotated
CT brain scans from patients with AIS, we designed a convolutional neural
network-based DL algorithm using routinely-collected CT brain scans from the
Third International Stroke Trial (IST-3), which were not acquired using strict
research protocols. The DL model aimed to detect AIS lesions and classify the
side of the brain affected. We explored the impact of AIS lesion features,
background brain appearances, and timing on DL performance. From 5772 unique CT
scans of 2347 AIS patients (median age 82), 54% had visible AIS lesions
according to expert labelling. Our best-performing DL method achieved 72%
accuracy for lesion presence and side. Lesions that were larger (80% accuracy)
or multiple (87% accuracy for two lesions, 100% for three or more), were better
detected. Follow-up scans had 76% accuracy, while baseline scans 67% accuracy.
Chronic brain conditions reduced accuracy, particularly non-stroke lesions and
old stroke lesions (32% and 31% error rates respectively). DL methods can be
designed for AIS lesion detection on CT using the vast quantities of
routinely-collected CT brain scan data. Ultimately, this should lead to more
robust and widely-applicable methods.

研究使用深度学习（DL）技术，设计基于计算机断层扫描（CT）的 DL 算法，从非严格研究协议中收集的 CT 脑部扫描数据中检测急性缺血性中风（AIS）病变，并分类受影响的脑部侧边。通过探索 AIS 病变特征、背景脑部外观和时间对 DL 性能的影响，最佳 DL 方法对病变存在和侧边分类达到了 72％的准确率，对大尺寸病变（80％准确率）和多发病变（87％二个病变、100％三个或以上病变的准确率）检测效果更好。