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.

翻译：计算机断层扫描（CT）常用于急性缺血性卒中（AIS）患者的影像学检查，但放射科医师对其解读费时且存在观察者间差异。深度学习（DL）技术可提供自动化CT脑扫描评估，但通常需要标注图像。为利用带有标签但未标注的AIS患者CT脑扫描数据开发深度学习方法，我们设计了一种基于卷积神经网络的深度学习算法，采用来自第三次国际卒中试验（IST-3）的常规采集CT脑扫描数据（非严格研究方案获取）。该深度学习模型旨在检测AIS病变并识别受累大脑侧别。我们探究了AIS病变特征、背景脑部表现及扫描时机对深度学习性能的影响。在2347例AIS患者（中位年龄82岁）的5772份独立CT扫描中，根据专家标注54%存在可见AIS病变。最优深度学习方法的病变存在及侧别识别准确率达72%。体积较大（准确率80%）或多发性（两个病变准确率87%，三个及以上为100%）的病变检测效果更佳。随访扫描准确率为76%，基线扫描为67%。慢性脑部病变会降低准确率，尤其非卒中病变和陈旧性卒中病变（错误率分别达32%和31%）。深度学习方法可利用海量常规采集的CT脑扫描数据实现AIS病变检测，这将最终推动更稳健且广泛适用性方法的开发。