Automated Vessel Occlusion Software in Acute Ischemic Stroke: Pearls and Pitfalls.
Authors
Affiliations (7)
Affiliations (7)
- 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.).
- Department of Radiology and Biomedical Imaging, University of California, San Francisco (K.N.).
- Departments of Neurosurgery and Neurology, Washington University School of Medicine in St Louis, Mallinckrodt Institute of Radiology, MO (A.R.C.).
- Department of Radiology, Stanford University, CA (J.J.H.).
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, AZ (A.J.).
- Russell H. Morgan Department of Radiology and Radiological Sciences, John Hopkins University School of Medicine, Baltimore, MD (V.Y.).
Abstract
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.