Schlossman Jacob, Ro Daniel, Salehi Shirin, Chow Daniel, Yu Wengui, Chang Peter D, Soun Jennifer E
Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, CA, United States.
University of California Irvine School of Medicine, Irvine, CA, United States.
Front Neurol. 2022 Oct 10;13:1026609. doi: 10.3389/fneur.2022.1026609. eCollection 2022.
Despite the availability of commercial artificial intelligence (AI) tools for large vessel occlusion (LVO) detection, there is paucity of data comparing traditional machine learning and deep learning solutions in a real-world setting. The purpose of this study is to compare and validate the performance of two AI-based tools (RAPID LVO and CINA LVO) for LVO detection.
This was a retrospective, single center study performed at a comprehensive stroke center from December 2020 to June 2021. CT angiography ( = 263) for suspected stroke were evaluated for LVO. RAPID LVO is a traditional machine learning model which primarily relies on vessel density threshold assessment, while CINA LVO is an end-to-end deep learning tool implemented with multiple neural networks for detection and localization tasks. Reasons for errors were also recorded.
There were 29 positive and 224 negative LVO cases by ground truth assessment. RAPID LVO demonstrated an accuracy of 0.86, sensitivity of 0.90, specificity of 0.86, positive predictive value of 0.45, and negative predictive value of 0.98, while CINA demonstrated an accuracy of 0.96, sensitivity of 0.76, specificity of 0.98, positive predictive value of 0.85, and negative predictive value of 0.97.
Both tools successfully detected most anterior circulation occlusions. RAPID LVO had higher sensitivity while CINA LVO had higher accuracy and specificity. Interestingly, both tools were able to detect some, but not all M2 MCA occlusions. This is the first study to compare traditional and deep learning LVO tools in the clinical setting.
尽管有用于大血管闭塞（LVO）检测的商业人工智能（AI）工具，但在实际应用中，比较传统机器学习和深度学习解决方案的数据却很匮乏。本研究的目的是比较并验证两种基于AI的工具（RAPID LVO和CINA LVO）用于LVO检测的性能。
这是一项回顾性单中心研究，于2020年12月至2021年6月在一家综合性卒中中心进行。对疑似卒中患者的CT血管造影（n = 263）进行LVO评估。RAPID LVO是一种传统的机器学习模型，主要依靠血管密度阈值评估，而CINA LVO是一种端到端的深度学习工具，通过多个神经网络实现检测和定位任务。还记录了错误原因。
经地面真值评估，有29例LVO阳性病例和224例LVO阴性病例。RAPID LVO的准确率为0.86，灵敏度为0.90，特异度为0.86，阳性预测值为0.45，阴性预测值为0.98；而CINA LVO的准确率为0.96，灵敏度为0.76，特异度为0.98，阳性预测值为0.85，阴性预测值为0.97。
两种工具均成功检测出了大多数前循环闭塞。RAPID LVO的灵敏度较高，而CINA LVO的准确率和特异度较高。有趣的是，两种工具都能检测出部分但并非全部的大脑中动脉M2段闭塞。这是第一项在临床环境中比较传统和深度学习LVO工具的研究。
