Objective Clot characteristics can provide information on the cause of cerebral artery occlusion and may guideline acute revascularization and secondary prevention strategies. 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 transmission profiles in external validation ((%)26 (68.4)15 (51.7)0.165Hypertension, (%)19 (50.0)15 (51.4)0.889Diabetes, (%)7 (18.4)8 (27.6)0.373Dyslipidemia, (%)5 (13.2)6 (20.7)0.41Atrial fibrillation, (%)NAPreviously diagnosed0 (0.0)13 (44.8)Newly detected0 (0.0)16 (55.2)Other causes of clotNAIntracranial atherosclerosis24 (63.2)0 (0.0)Thromboembolism from carotid plaque6 (15.8)0 (0.0)Other and undetermined sources1 8 (21.0)0 (0.0)Initial NIHSS score12 [9C16]15 [12C18]0.038Intravenous tPA, (%)23 (60.5)22 (75.9)0.185Glucose (mg/mL), mean??SD133.2??42.7118.0??20.70.083Systolic blood BAY 73-6691 racemate pressure (mmHg), mean??SD139.7??21.6140.9??17.60.814Symptom to ER introduction, median (IQR)53 [28C98]45 [31C123]0.537Symptom to GRE imaging, median (IQR)124 [94C183]116 [101C187]0.368Symptom to groin puncture (min), median (IQR)190 [145C227]180 [157C240]0.502 Open in a separate window SD, standard deviation; tPA, NIHSS, National Institutes of Health stroke scale; Tissue plasminogen activator; ER, emergency room; IQR, interquartile range; GRE, gradient echo. 1Paradoxical embolism in 2, aortic arch atheroma in 1, and undetermined source in 5. The discordant cases were re\examined and final blooming artifact was determined by consensus. The sensitivity and specificity for underlying atrial fibrillation were 0.79 and 0.63, respectively (area under the curve, 0.78). For the ML classification techniques, the fivefold cross\validation BAY 73-6691 racemate resulted in mean accuracy (standard deviation) of 75.4 (7.7) % for random forest, 78.7 (9.4) % BAY 73-6691 racemate for support vector machine, 75.5 (10.4) % for artificial neural network, and 77.3 (9.4) % for logistic regression, respectively. The external BAY 73-6691 racemate validation resulted in the areas under the curve of 0.87C0.93 and 0.91C0.93 for observer 1 and observer 2, respectively (Fig.?2). Open in a separate window Physique 2 ROC curves BAY 73-6691 racemate for external validation ((%)23 (60.5)22 (75.9)0.185Treatment modality, (%)Stentriever31 (81.6)24 (82.8)0.901Stent7 (18.4)1 (3.4)0.061Glycogen IIb/IIIa blocker11 (28.9)2 (6.9)0.024Procedural event, median [IQR]Number of retrieval passes3 [3C4]2 [1C3] 0.001Number of reocclusions during process2 [0C4]0 [0C0] 0.001Total procedure time (min)101.6??46.182.4??36.40.07Procedural outcomes, (%)mTICI 2b or 316 (42.1)21 (72.4)0.013by stentriever3 (7.9)19 (65.5) 0.001by other modalityStent7 (18.4)1 (3.4)0.061Glycogen IIb/IIIa blocker6 (15.8)1 (3.4)0.102 Open Vamp5 in a separate window tPA, Tissue plasminogen activator; IQR, interquartile range; mTICI, altered Treatment in Cerebral Infarction. Open in a separate window Physique 3 Representative cases of GRE vessel transmission change after successful endovascular clot retrieval in atrial fibrillation and intracranial atherosclerosis patients. (A) Clot transmission analysis prior to endovascular thrombectomy showing W shaped transmission intensity. (B) Retrieved reddish clots. (C) Resolved W indication after effective removal of atrial fibrillation\related clot. (D) Clot indication analysis ahead of endovascular thrombectomy displaying non\W\shaped signal strength. (E) Retrieved white clots. (F) Heterogeneous dark indication after effective recanalization of atherosclerotic occlusion with crisis stenting. GRE, gradient echo. Conversation The major findings of this study were as follows. In individual with acute MCA occlusion, pretreatment GRE image\centered clot analysis was feasible, and a machine learning\centered clot analysis algorithm expected atrial fibrillation with high accuracy. Response to EVT and the need for therapy differed between individuals with and without atrial fibrillation as well as according to the characteristics of clot imaging. This suggests that utilization of a machine learning algorithm for evaluation of clot characteristics based on GRE imaging could be helpful in selecting an appropriate EVT modality and may lead to faster recanalization in individuals with MCA occlusion. There have been attempts to visualize the clot in individuals with acute ischemic stroke. Clot characteristics can be indicated from the size/burden and composition, which may be related to etiopathologic subtypes of stroke. The importance of clot burden in intravenous thrombolysis has been evaluated with computed tomography (CT) and MR.