Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning
Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning
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DOI：
10.1073/pnas.1908021116
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发表时间：
2019-11-05
影响因子：
11.1
通讯作者：
Yuh, Esther L.
中科院分区：
文献类型：
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作者：
Kuo, Weicheng;Hane, Christian;Yuh, Esther L.
头部的计算机断层扫描(CT)在世界范围内被用于诊断神经紧急情况。然而，解释这些扫描需要专业知识，即使是训练有素的专家也可能错过危及生命的细微发现。对于头部CT来说，一个独特的挑战是以完美或接近完美的灵敏度和非常高的特异度识别多层断层(三维[3D])成像方式上通常是微小的细微异常，其特征是软组织对比度差，使用当前的低辐射剂量方案的低信噪比，以及高伪影发生率。我们用在加州大学旧金山分校及其附属医院进行的4396次头部CT扫描训练了一个完全卷积神经网络，并将该算法的性能与4名美国放射学委员会(ABR)认证的放射科医生在200次随机选择的头部CT扫描的独立测试集上的性能进行了比较。我们的算法在这一临床应用中表现出最高的准确性，在识别急性颅内出血阳性检查方面，受试者工作特征曲线下面积(AUC)为0.991+/-0.006，也超过了4名放射科医生中的2名。我们展示了一个端到端的网络，它执行联合分类和分割，检查级别的分类与专家相当，此外还有对异常的健壮定位，包括一些放射科医生遗漏的异常，这两个都是这一应用程序的关键要素。
Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique challenge is to identify, with perfect or near-perfect sensitivity and very high specificity, often small subtle abnormalities on a multislice cross-sectional (three-dimensional [3D]) imaging modality that is characterized by poor soft tissue contrast, low signal-to-noise using current low radiation-dose protocols, and a high incidence of artifacts. We trained a fully convolutional neural network with 4,396 head CT scans performed at the University of California at San Francisco and affiliated hospitals and compared the algorithm's performance to that of 4 American Board of Radiology (ABR) certified radiologists on an independent test set of 200 randomly selected head CT scans. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 +/- 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists. We demonstrate an end-to-end network that performs joint classification and segmentation with examination-level classification comparable to experts, in addition to robust localization of abnormalities, including some that are missed by radiologists, both of which are critically important elements for this application.