| Title: |
Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes |
| Authors: |
Jennifer E. Soun, Anna Zolyan, Joel McLouth, Sebastian Elstrott, Masaki Nagamine, Conan Liang, Farideh H. Dehkordi-Vakil, Eleanor Chu, David Floriolli, Edward Kuoy, John Joseph, Nadine Abi-Jaoudeh, Peter D. Chang, Wengui Yu, Daniel S. Chow |
| Source: |
Frontiers in Neurology, Vol 14 (2023) |
| Publisher Information: |
Frontiers Media S.A., 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Neurology. Diseases of the nervous system |
| Subject Terms: |
artificial intelligence, large vessel occlusion, stroke, machine learning, CT angiography, Neurology. Diseases of the nervous system, RC346-429 |
| More Details: |
PurposeAutomated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world setting. The purpose of this study was to evaluate the automated LVO detection tool’s impact on acute stroke workflow and clinical outcomes.Materials and methodsConsecutive patients with a computed tomography angiography (CTA) presenting with suspected acute ischemic stroke were compared before and after the implementation of an AI tool, RAPID LVO (RAPID 4.9, iSchemaView, Menlo Park, CA). Radiology CTA report turnaround times (TAT), door-to-treatment times, and the NIH stroke scale (NIHSS) after treatment were evaluated.ResultsA total of 439 cases in the pre-AI group and 321 cases in the post-AI group were included, with 62 (14.12%) and 43 (13.40%) cases, respectively, receiving acute therapies. The AI tool demonstrated a sensitivity of 0.96, a specificity of 0.85, a negative predictive value of 0.99, and a positive predictive value of 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI vs. 22 min for post-AI, p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1664-2295 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fneur.2023.1179250/full; https://doaj.org/toc/1664-2295 |
| DOI: |
10.3389/fneur.2023.1179250 |
| Access URL: |
https://doaj.org/article/7a1ab3e448cf4cf696da1ba9b5e8a8bf |
| Accession Number: |
edsdoj.7a1ab3e448cf4cf696da1ba9b5e8a8bf |
| Database: |
Directory of Open Access Journals |
