Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function-'time is brain'1-5. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging6-10. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data11-15. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework16. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment.

中文翻译：

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颅神经图像的自动深层神经网络监视，用于急性神经系统事件。

快速诊断和治疗中风，出血和脑积水等急性神经系统疾病，对于取得积极成果和维持神经功能至关重要-“时间就是大脑” 1-5。尽管这些病症通常可以通过症状来识别，但诊断的关键手段是快速成像6-10。颅骨成像中计算机辅助的急性神经系统事件的监测具有对放射学工作流进行分类的潜力，从而减少了治疗时间并改善了结局。大量的临床工作集中在计算机辅助诊断（CAD）上，而体积图像分析中的技术工作主要集中在分割上。3D卷积神经网络（3D-CNN）主要用于3D建模和光检测与测距（LiDAR）数据的监督分类11-15。这里，我们演示了执行弱监督分类的3D-CNN架构，以筛查头部CT图像是否存在急性神经系统事件。从包括37,236个头部CT的临床放射学数据集中自动学习特征，并使用半监督自然语言处理（NLP）框架对其进行注释16。我们通过在模拟临床环境中进行的随机，双盲，前瞻性试验，证明了我们的分类放射学工作流程方法的有效性，并将诊断时间从几分钟缩短到了几秒钟。236个头部CT，并使用半监督自然语言处理（NLP）框架进行注释16。我们通过在模拟临床环境中进行的随机，双盲，前瞻性试验，证明了我们的分类放射学工作流程方法的有效性，并将诊断时间从几分钟缩短到了几秒钟。236个头部CT，并使用半监督自然语言处理（NLP）框架进行注释16。我们通过在模拟临床环境中进行的随机，双盲，前瞻性试验，证明了我们的分类放射学工作流程方法的有效性，并将诊断时间从几分钟缩短到了几秒钟。