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Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke
P. Cimflova, R. Golan, JM. Ospel, A. Sojoudi, C. Duszynski, I. Elebute, H. El-Hariri, S. Hossein Mousavi, LASM. Neto, N. Pinky, B. Beland, F. Bala, NR. Kashani, W. Hu, M. Joshi, W. Qiu, BK. Menon
Jazyk angličtina Země Německo
Typ dokumentu časopisecké články
NLK
ProQuest Central
od 2002-01-01 do Před 1 rokem
CINAHL Plus with Full Text (EBSCOhost)
od 2008-01-01 do Před 1 rokem
Medline Complete (EBSCOhost)
od 2003-01-01 do Před 1 rokem
Nursing & Allied Health Database (ProQuest)
od 2002-01-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2002-01-01 do Před 1 rokem
- MeSH
- arteriální okluzní nemoci * MeSH
- cévní mozková příhoda * diagnostické zobrazování MeSH
- CT angiografie metody MeSH
- infarkt arteria cerebri media diagnostické zobrazování MeSH
- ischemická cévní mozková příhoda * MeSH
- ischemie mozku * MeSH
- lidé MeSH
- retrospektivní studie MeSH
- software MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
PURPOSE: CT angiography (CTA) is the imaging standard for large vessel occlusion (LVO) detection in patients with acute ischemic stroke. StrokeSENS LVO is an automated tool that utilizes a machine learning algorithm to identify anterior large vessel occlusions (LVO) on CTA. The aim of this study was to test the algorithm's performance in LVO detection in an independent dataset. METHODS: A total of 400 studies (217 LVO, 183 other/no occlusion) read by expert consensus were used for retrospective analysis. The LVO was defined as intracranial internal carotid artery (ICA) occlusion and M1 middle cerebral artery (MCA) occlusion. Software performance in detecting anterior LVO was evaluated using receiver operator characteristics (ROC) analysis, reporting area under the curve (AUC), sensitivity, and specificity. Subgroup analyses were performed to evaluate if performance in detecting LVO differed by subgroups, namely M1 MCA and ICA occlusion sites, and in data stratified by patient age, sex, and CTA acquisition characteristics (slice thickness, kilovoltage tube peak, and scanner manufacturer). RESULTS: AUC, sensitivity, and specificity overall were as follows: 0.939, 0.894, and 0.874, respectively, in the full cohort; 0.927, 0.857, and 0.874, respectively, in the ICA occlusion cohort; 0.945, 0.914, and 0.874, respectively, in the M1 MCA occlusion cohort. Performance did not differ significantly by patient age, sex, or CTA acquisition characteristics. CONCLUSION: The StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.
Circle Neurovascular Imaging Inc Calgary AB Canada
Department of Radiology University Hospital of Basel Basel Switzerland
Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary Canada
Citace poskytuje Crossref.org
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- $a Cimflova, Petra $u Department of Clinical Neurosciences and Radiology, Cumming School of Medicine, Foothills Medical Centre, University of Calgary, 1403 29th Street NW, Calgary, AB, T2N 2T9, Canada. petracimflova@gmail.com $u Department of Medical Imaging, St. Anne's University Hospital Brno and Faculty of Medicine, Masaryk University Brno, Brno, Czech Republic. petracimflova@gmail.com $1 https://orcid.org/000000018058383X
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- $a PURPOSE: CT angiography (CTA) is the imaging standard for large vessel occlusion (LVO) detection in patients with acute ischemic stroke. StrokeSENS LVO is an automated tool that utilizes a machine learning algorithm to identify anterior large vessel occlusions (LVO) on CTA. The aim of this study was to test the algorithm's performance in LVO detection in an independent dataset. METHODS: A total of 400 studies (217 LVO, 183 other/no occlusion) read by expert consensus were used for retrospective analysis. The LVO was defined as intracranial internal carotid artery (ICA) occlusion and M1 middle cerebral artery (MCA) occlusion. Software performance in detecting anterior LVO was evaluated using receiver operator characteristics (ROC) analysis, reporting area under the curve (AUC), sensitivity, and specificity. Subgroup analyses were performed to evaluate if performance in detecting LVO differed by subgroups, namely M1 MCA and ICA occlusion sites, and in data stratified by patient age, sex, and CTA acquisition characteristics (slice thickness, kilovoltage tube peak, and scanner manufacturer). RESULTS: AUC, sensitivity, and specificity overall were as follows: 0.939, 0.894, and 0.874, respectively, in the full cohort; 0.927, 0.857, and 0.874, respectively, in the ICA occlusion cohort; 0.945, 0.914, and 0.874, respectively, in the M1 MCA occlusion cohort. Performance did not differ significantly by patient age, sex, or CTA acquisition characteristics. CONCLUSION: The StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.
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