Aziz Yasmin N, Sriwastwa Aakanksha, Nael Kambiz, Harker Pablo, Mistry Eva A, Khatri Pooja, Chatterjee Arindam R, Heit Jeremy J, Jadhav Ashutosh, Yedavalli Vivek, Vagal Achala S
Department of Neurology and Rehabilitation Medicine, University of Cincinnati, OH. (Y.N.A., P.H., E.A.M., P.K.).
Department of Radiology, University of Cincinnati, OH. (A.S., A.S.V.).
Stroke. 2025 Jun 9. doi: 10.1161/STROKEAHA.124.049555.
Software programs leveraging artificial intelligence to detect vessel occlusions are now widely available to aid in stroke triage. Given their proprietary use, there is a surprising lack of information regarding how the software works, who is using the software, and their performance in an unbiased real-world setting. In this educational review of automated vessel occlusion software, we discuss emerging evidence of their utility, underlying algorithms, real-world diagnostic performance, and limitations. The intended audience includes specialists in stroke care in neurology, emergency medicine, radiology, and neurosurgery. Practical tips for onboarding and utilization of this technology are provided based on the multidisciplinary experience of the authorship team.
利用人工智能检测血管闭塞的软件程序现已广泛应用于中风分诊。鉴于其专有使用情况,令人惊讶的是,关于该软件的工作原理、谁在使用该软件以及它们在无偏见的现实环境中的性能,缺乏相关信息。在本次对自动血管闭塞软件的教育性综述中,我们讨论了其效用、基础算法、现实诊断性能及局限性的新证据。目标受众包括神经科、急诊科、放射科和神经外科的中风护理专家。基于作者团队的多学科经验,提供了引入和使用该技术的实用技巧。