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Characterization of clot composition in acute cerebral infarct using machine learning techniques

 Jong‐Won Chung  ;  Yoon‐Chul Kim  ;  Jihoon Cha  ;  Eun‐Hyeok Choi  ;  Byung Moon Kim  ;  Woo‐Keun Seo  ;  Gyeong‐Moon Kim  ;  Oh Young Bang 
 Annals of Clinical and Translational Neurology, Vol.6(4) : 739-747, 2019 
Journal Title
 Annals of Clinical and Translational Neurology 
Issue Date
Objective: Clot characteristics can provide information on the cause of cerebral artery occlusion and may guide acute revascularization and secondary prevention strategies. We developed a rapid automated clot analysis system using machine learning (ML) and validated its accuracy in patients undergoing endovascular treatment. Methods: Pre-endovascular treatment gradient echo (GRE) images from consecutive patients with middle cerebral artery occlusion were utilized to develop and validate an ML system to predict whether atrial fibrillation (AF) was the underlying cause of ischemic stroke. The accuracy of the ML algorithm was compared with that of visual inspection by neuroimaging specialists for the presence of blooming artifact. Endovascular procedures and outcomes were compared in patients with and without AF. Results: Of 67 patients, 29 (43.3%) had AF. Of these, 13 had known AF and 16 were newly diagnosed with cardiac monitoring. By visual inspection, interrater correlation for blooming artifact was 0.73 and sensitivity and specificity for AF were 0.79 and 0.63, respectively. For AF classification, the ML algorithms yielded an average accuracy of > 75.4% in fivefold cross-validation with clot signal profiles obtained from 52 patients and an area under the curve >0.87 for the average AF probability from five signal profiles in external validation (n = 15). Analysis with an in-house interface took approximately 3 min per patient. Absence of AF was associated with increased number of passes by stentriever, high reocclusion frequency, and additional use of rescue stenting and/or glycogen IIb/IIIa blocker for recanalization. Interpretation: ML-based rapid clot analysis is feasible and can identify AF with high accuracy, enabling selection of endovascular treatment strategy.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Byung Moon(김병문) ORCID logo https://orcid.org/0000-0001-8593-6841
Cha, Jihoon(차지훈)
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