Abstract Details

Title Can Artificial Intelligence to Detect Large Vessel Occlusion Improve Patient Care? A Systematic Review and Meta-analysis

Topic Cerebrovascular Disease and Interventional Neurology

Presentation(s) P5 - Poster Session 5 (5:30 PM-6:30 PM)

Poster/Presentation
Number 5-026

Objective We aimed to perform a systematic review and meta-analysis comparing artificial intelligence (AI)-assisted radiological assessment versus standard assessment of large vessel occlusion (LVO) in patients with acute stroke.

Background LVO accounts for 40% of all ischemic strokes. While recent studies have shown that AI softwares have good accuracy in identifying LVOs on computed tomography angiograms (CTA), whether this improves workflow times and patient outcomes is still unclear.

Design/Methods PubMed, Embase, and Web of Science were systematically searched for observational and randomized controlled trials that compared initial radiological assessment assisted by AI softwares versus standard assessment of patients with acute LVO strokes. Results were pooled as mean differences for continuous outcomes, and odds ratio (OR) for dichotomous outcomes, along with 95% confidence intervals (CI). A random-effects model was used for all analyses.

Results A total of 8 studies comprising 984 patients were included, of whom 54.7% (538) had AI-assisted radiological assessment. AI consistently improved treatment times when compared to standard assessment, as evidenced by a mean reduction of 24.79 minutes in door-to-groin time (95% CI -42.85 to -6.73; p<0.01; Figure 1) and a reduction of 14.99 minutes in CTA to reperfusion (95% CI -28.45 to -1.53; p=0.03). Functional independence, defined as a modified Rankin scale 0-2, occurred more frequently in the AI-supported group when compared to the standard workflow (OR 1.58; 95% CI 1.01 to 2.47; p=0.04; Figure 2). Mortality, however, was similar among groups (OR 0.71; 95% CI 0.27 to 1.88; p=0.49).

Conclusions The incorporation of AI softwares for LVO detection in acute ischemic stroke enhanced workflow efficiency, reduced treatment duration, and improved rates of functional independence.

Authors/Disclosures
Felipe A. Gouhie PRESENTER	Mr. Gouhie has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Marjan Farma.
Julyana M. Dantas, MD	Dr. Dantas has nothing to disclose.
Giovana B. Ribeiro, MD (Unicamp - Hospital de Clínicas)	Dr. Ribeiro has nothing to disclose.
Caroline Dagostin	No disclosure on file
Antonio Mutarelli	ANTONIO MUTARELLI has nothing to disclose.
Pedro Romeiro	No disclosure on file
Giulia Soares	No disclosure on file
Agostinho C. Pinheiro, MD (Mass General Brigham Neurology Residency)	Dr. Pinheiro has nothing to disclose.

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