McLouth Joel, Elstrott Sebastian, Chaibi Yasmina, Quenet Sarah, Chang Peter D, Chow Daniel S, Soun Jennifer E
Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.
Avicenna.AI, La Ciotat, France.
Front Neurol. 2021 Apr 29;12:656112. doi: 10.3389/fneur.2021.656112. eCollection 2021.
Recently developed machine-learning algorithms have demonstrated strong performance in the detection of intracranial hemorrhage (ICH) and large vessel occlusion (LVO). However, their generalizability is often limited by geographic bias of studies. The aim of this study was to validate a commercially available deep learning-based tool in the detection of both ICH and LVO across multiple hospital sites and vendors throughout the U.S. This was a retrospective and multicenter study using anonymized data from two institutions. Eight hundred fourteen non-contrast CT cases and 378 CT angiography cases were analyzed to evaluate ICH and LVO, respectively. The tool's ability to detect and quantify ICH, LVO, and their various subtypes was assessed among multiple CT vendors and hospitals across the United States. Ground truth was based off imaging interpretations from two board-certified neuroradiologists. There were 255 positive and 559 negative ICH cases. Accuracy was 95.6%, sensitivity was 91.4%, and specificity was 97.5% for the ICH tool. ICH was further stratified into the following subtypes: intraparenchymal, intraventricular, epidural/subdural, and subarachnoid with true positive rates of 92.9, 100, 94.3, and 89.9%, respectively. ICH true positive rates by volume [small (<5 mL), medium (5-25 mL), and large (>25 mL)] were 71.8, 100, and 100%, respectively. There were 156 positive and 222 negative LVO cases. The LVO tool demonstrated an accuracy of 98.1%, sensitivity of 98.1%, and specificity of 98.2%. A subset of 55 randomly selected cases were also assessed for LVO detection at various sites, including the distal internal carotid artery, middle cerebral artery M1 segment, proximal middle cerebral artery M2 segment, and distal middle cerebral artery M2 segment with an accuracy of 97.0%, sensitivity of 94.3%, and specificity of 97.4%. Deep learning tools can be effective in the detection of both ICH and LVO across a wide variety of hospital systems. While some limitations were identified, specifically in the detection of small ICH and distal M2 occlusion, this study highlights a deep learning tool that can assist radiologists in the detection of emergent findings in a variety of practice settings.
最近开发的机器学习算法在检测颅内出血（ICH）和大血管闭塞（LVO）方面表现出强大的性能。然而，它们的通用性往往受到研究地理偏差的限制。本研究的目的是验证一种基于深度学习的商用工具在全美国多个医院站点和供应商中检测ICH和LVO的能力。这是一项回顾性多中心研究，使用了来自两个机构的匿名数据。分别分析了814例非增强CT病例和378例CT血管造影病例以评估ICH和LVO。在美国多个CT供应商和医院中评估了该工具检测和量化ICH、LVO及其各种亚型的能力。金标准基于两位具有董事会认证的神经放射科医生的影像解读。有255例ICH阳性病例和559例ICH阴性病例。ICH检测工具的准确率为95.6%，灵敏度为91.4%，特异性为97.5%。ICH进一步分为以下亚型：脑实质内、脑室内、硬膜外/硬膜下和蛛网膜下，真阳性率分别为92.9%、100%、94.3%和89.9%。按体积[小（<5 mL）、中（5 - 25 mL）和大（>25 mL）]划分的ICH真阳性率分别为71.8%、100%和100%。有156例LVO阳性病例和222例LVO阴性病例。LVO检测工具的准确率为98.1%，灵敏度为98.1%，特异性为98.2%。还在包括颈内动脉远端、大脑中动脉M1段、大脑中动脉M2段近端和大脑中动脉M2段远端等不同部位对55例随机选择的病例子集进行了LVO检测评估，准确率为97.0%，灵敏度为94.3%，特异性为97.4%。深度学习工具在各种医院系统中检测ICH和LVO方面都可能有效。虽然发现了一些局限性，特别是在小ICH和M2段远端闭塞的检测方面，但本研究突出了一种深度学习工具，它可以帮助放射科医生在各种实践环境中检测紧急情况。
