Abstract Details

Title Assessing Real-world Artificial Intelligence Use Among United States Stroke Centers

Topic Cerebrovascular Disease and Interventional Neurology

Presentation(s) P3 - Poster Session 3 (5:30 PM-6:30 PM)

Poster/Presentation
Number 5-023

Background Advanced imaging with AI and perfusion technology can automate assessment of large vessel occlusion and tissue damage in acute stroke. Extent of real-world use is not well known.

Objective We assessed real-world use of advanced imaging in acute stroke protocols and characteristics of US stroke centers that do and do not use artificial intelligence (AI).

Design/Methods We utilized an ongoing electronic survey of United States hospitals that treat with thrombolysis and/or thrombectomy (EVT). Hospitals with publicly available electronic contact information were contacted, and respondents included stroke directors or coordinators. We performed retrospective cross-sectional analysis of 20% (n=141) of responding stroke centers at the time of interim analysis. We analyzed questions assessing imaging utilization, center certification and resources, self-reported stroke treatment times, and stroke treatment volumes. We used descriptive analysis and chi-square tests of independence to compare centers that utilize AI to centers that do not.

Results

Among 141 stroke centers, 84.4% (n=119) utilized AI and 15.6% (n=22) did not. Of the AI centers, 79.8% (n=95) performed EVT in-house and 18.5% (n=22) transferred patients to outside hospitals for EVT. Centers that utilized AI were more commonly comprehensive or thrombectomy-capable centers (72.27% vs 18.18% p=<0.001), served populations of >50,000 (77.31% vs 45.45%, p=0.005), and treated >50 stroke patients annually with thrombolysis (68.91% vs 31.82%, p=0.001) and EVT (56.30% vs 18.18%, p=<0.001). Additionally, AI centers more commonly used CT perfusion in their initial acute stroke imaging protocol (32.37% vs 4.55% p=0.03). There was no difference in self-report median door in-door out times between AI and non-AI centers (p=0.98).

Conclusions These limited data suggest that AI use is widespread among certified high-volume stroke centers that perform EVT. Efforts should be made to help smaller and lower resourced centers obtain AI to aid in large vessel occlusion detection.

Authors/Disclosures
Nicholas Buonafede PRESENTER	No disclosure on file
Diandra Adu-Kyei	Miss Adu-Kyei has nothing to disclose.
Jaan Nandwani	Miss Nandwani has nothing to disclose.
Mandip S. Dhamoon, MD, MPH	Dr. Dhamoon has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Faegre Baker Daniels LLP. Dr. Dhamoon has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Wellstar Health System Inc. Dr. Dhamoon has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Fabiani Cohen & Hall, LLP. Dr. Dhamoon has received personal compensation in the range of $5,000-$9,999 for serving as an Expert Witness for Kramer, Dillof, Livingston & Moore. Dr. Dhamoon has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Robins Kaplan. Dr. Dhamoon has received personal compensation in the range of $5,000-$9,999 for serving as an Expert Witness for Parker Waichman LLP. Dr. Dhamoon has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Heidell, Pittoni, Murphy & Bach, LLP.
J Mocco	No disclosure on file
Nathalie Jette, MD, MSc, FRCPC, FAAN (Icahn School of Medicine at Mount Sinai)	Dr. Jette has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for ILAE Epilepsia. The institution of Dr. Jette has received research support from NIH. The institution of Dr. Jette has received research support from AES.
Laura Stein, MD (Mount Sinai School of Medicine)	The institution of Dr. Stein has received research support from American Heart Association.