Journal
STROKE
Volume 51, Issue 10, Pages 3133-3137Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/STROKEAHA.120.030326
Keywords
area under the curve; deep learning; middle cerebral artery; patient transfer; thrombectomy
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Funding
- Centro para el Desarrollo Tecnologico Industrial (CDTI)
- Agencia per la Competitivitat de l'Empresa (ACCIO), Generalitat de Catalunya
- Instituto de Salud Carlos III
- European Social Fund (The ESFInvesting in your future)
- Redes Tematicas de Investigacion Cooperativa [RETICS-INVICTUS-PLUS RD0016/0019/0010-0002-0014]
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Background and Purpose: Reliable recognition of large vessel occlusion (LVO) on noncontrast computed tomography (NCCT) may accelerate identification of endovascular treatment candidates. We aim to validate a machine learning algorithm (MethinksLVO) to identify LVO on NCCT. Methods: Patients with suspected acute stroke who underwent NCCT and computed tomography angiography (CTA) were included. Software detection of LVO (MethinksLVO) on NCCT was tested against the CTA readings of 2 experienced radiologists (NR-CTA). We used a deep learning algorithm to identify clot signs on NCCT. The software image output trained a binary classifier to determine LVO on NCCT. We studied software accuracy when adding National Institutes of Health Stroke Scale and time from onset to the model (MethinksLVO+). Results: From 1453 patients, 823 (57%) had LVO by NR-CTA. The area under the curve for the identification of LVO with MethinksLVO was 0.87 (sensitivity: 83%, specificity: 71%, positive predictive value: 79%, negative predictive value: 76%) and improved to 0.91 with MethinksLVO+ (sensitivity: 83%, specificity: 85%, positive predictive value: 88%, negative predictive value: 79%). Conclusions: In patients with suspected acute stroke, MethinksLVO software can rapidly and reliably predict LVO. MethinksLVO could reduce the need to perform CTA, generate alarms, and increase the efficiency of patient transfers in stroke networks.
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