Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which is routinely diagnosed using non-contrast head CT imaging. The diagnostic accuracy of acute ICH on CT varies greatly among radiologists due to the difficulty of interpreting subtle findings and the time pressure associated with the ever-increasing workload. The use of artificial intelligence technology may help automate the process and assist radiologists for more prompt and better decision-making. In this work, we design a deep learning approach that mimics the interpretation process of radiologists, and combines a 2D CNN model and two sequence models to achieve accurate acute ICH detection and subtype classification. Being developed using the extensive 2019-RSNA Brain CT Hemorrhage Challenge dataset with over 25000 CT scans, our deep learning algorithm can accurately classify the acute ICH and its five subtypes with AUCs of 0.988 (ICH), 0.984 (EDH), 0.992 (IPH), 0.996 (IVH), 0.985 (SAH), and 0.983 (SDH), respectively, reaching the accuracy level of expert radiologists. Our method won 1st place among 1345 teams from 75 countries in the RSNA challenge. We have further evaluated our algorithm on two independent external validation datasets with 75 and 491 CT scans, respectively, and our method maintained high AUCs of 0.964 and 0.949 for acute ICH detection. These results have demonstrated the high performance and robust generalization ability of our proposed method, which makes it a useful second-read or triage tool that can facilitate routine clinical applications.

急性颅内出血 (ICH) 是一种危及生命的疾病，需要紧急医疗护理，通常使用非对比头部 CT 成像进行诊断。由于难以解释细微的发现以及与不断增加的工作量相关的时间压力，放射科医生对 CT 急性脑出血的诊断准确性差异很大。人工智能技术的使用可能有助于实现流程自动化，并协助放射科医生做出更迅速、更好的决策。在这项工作中，我们设计了一种模仿放射科医生判读过程的深度学习方法，并结合 2D CNN 模型和两个序列模型来实现准确的急性 ICH 检测和亚型分类。我们的深度学习算法使用包含超过 25000 个 CT 扫描的广泛 2019-RSNA 脑 CT 出血挑战数据集开发，可以准确分类急性 ICH 及其五种亚型，AUC 分别为 0.988 (ICH)、0.984 (EDH)、0.992 (IPH) 、0.996（IVH）、0.985（SAH）和0.983（SDH），分别达到专家放射科医生的准确水平。我们的方法在 RSNA 挑战赛中从来自 75 个国家的 1345 个团队中获得了第一名。我们在两个独立的外部验证数据集上进一步评估了我们的算法，分别进行了 75 次和 491 次 CT 扫描，我们的方法在急性 ICH 检测中保持了 0.964 和 0.949 的高 AUC。这些结果证明了我们提出的方法的高性能和强大的泛化能力，这使其成为有用的二次阅读或分类工具，可以促进常规临床应用。