Detection of large vessel occlusion using artificial intelligence tools: A systematic review and meta-analysis.
Authors
Affiliations (10)
Affiliations (10)
- Universidade Federal do Rio Grande do Norte, Natal, Brazil. [email protected].
- Universidade Estadual de Campinas, Campinas, Brazil.
- Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
- Universidade do Extremo Sul Catarinense, Criciúma, Brazil.
- UNIMA/AFYA - Centro Universitário de Maceió, Maceió, Brazil.
- UNIMES - Universidade Metropolitana de Santos, Santos, Brazil.
- Universidade Federal de Campina Grande, Campina Grande, Brazil.
- Department of Neurology, Massachusetts General Hospital, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Internal Medicine, Elmhurst Hospital Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Surgery, Division of Neurosurgery, Universidade Federal de Goiás, Goiânia, Brazil.
Abstract
Large vessel occlusion (LVO) accounts for a third of all ischemic strokes. Artificial intelligence (AI) has shown good accuracy in identifying LVOs on computed tomography angiograms (CTA). We sought to analyze whether AI-adjudicated CTA improves workflow times and clinical outcomes in patients with confirmed LVOs. We systematically searched PubMed, Embase, and Web of Science for studies comparing initial radiological assessment assisted by AI softwares versus standard assessment of patients with acute LVO strokes. Results were pooled using a random-effects model as mean differences for continuous outcomes or odds ratio (OR) for dichotomous outcomes, along with 95% confidence intervals (CI). We included 9 studies comprising 1,270 patients, of whom 671 (52.8%) had AI-assisted radiological assessment. AI consistently improved treatment times when compared to standard assessment, as evidenced by a mean reduction of 20.55 minutes in door-to-groin time (95% CI -36.69 to -4.42 minutes; p<0.01) and a reduction of 14.99 minutes in CTA to reperfusion (95% CI -28.45 to -1.53 minutes; p=0.03). Functional independence, defined as a modified Rankin scale 0-2, occurred at similar rates in the AI-supported group and with the standard workflow (OR 1.27; 95% CI 0.92 to 1.76; p=0.14), as did mortality (OR 0.71; 95% CI 0.27 to 1.88; p=0.49). The incorporation of AI softwares for LVO detection in acute ischemic stroke enhanced workflow efficiency and was associated with decreased time to treatment. However, AI did not improve clinical outcomes as compared with standard assessment.