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Abstract No. 240 Automated outcome prediction in mechanical thrombectomy for acute large vessel ischemic stroke using 3D convolutional neural networks applied to CT angiography
Accurate prediction of clinical outcomes of acute large vessel occlusions (LVO) would lead to better patient selection and treatment planning for mechanical thrombectomy (MT). We developed a deep learning pipeline that predicts thrombectomy passes, immediate perfusion quality via the Thrombolysis in Cerebral Infarction (TICI) score and functional outcome via the 30-day modified Rankin Scale (mRS).
Source: Journal of Vascular and Interventional Radiology : JVIR - June 1, 2022 Category: Radiology Authors: L. Tran, S. Meng, P. Wang, I. Pan, T. Yi, R. Wang, Z. Jiao, H. Bai Source Type: research

A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke
This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) and compare its performance against experts ’ manual grading. Among consecutive LVO-AIS at three medical center sites, DSC-MRP data were processed to generate collateral flow maps consisting of arterial, capillary, and venous phases. With the use of expert readings as a reference, a DL model was developed to analyze collateral status with o utput classified into good and...
Source: Translational Stroke Research - May 21, 2022 Category: Neurology Source Type: research

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach
Ann Biomed Eng. 2022 Apr 1. doi: 10.1007/s10439-022-02956-7. Online ahead of print.ABSTRACTAssessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pr...
Source: Annals of Biomedical Engineering - April 2, 2022 Category: Biomedical Engineering Authors: Ivan Benemerito Ana Paula Narata Andrew Narracott Alberto Marzo Source Type: research

Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients
CONCLUSIONS: This study demonstrates feasibility of developing a new quantitative imaging method and marker to predict AIS patients' prognosis in the hyperacute stage, which can help clinicians optimally treat and manage AIS patients.PMID:35213340 | DOI:10.3233/XST-221138
Source: Journal of X-Ray Science and Technology - February 25, 2022 Category: Radiology Authors: Gopichandh Danala Bappaditya Ray Masoom Desai Morteza Heidari Seyedehnafiseh Mirniaharikandehei Sai Kiran R Maryada Bin Zheng Source Type: research

Effects of Repetitive Peripheral Sensory Stimulation in the Subacute and Chronic Phases After Stroke: Study Protocol for a Pilot Randomized Trial
DiscussionThe results of this study are relevant to inform future clinical trials to tailor RPSS to patients more likely to benefit from this intervention.Trial RegistrationNCT03956407.
Source: Frontiers in Neurology - February 16, 2022 Category: Neurology Source Type: research

U-net Models Based on Computed Tomography Perfusion Predict Tissue Outcome in Patients with Different Reperfusion Patterns
AbstractEvaluation of cerebral perfusion is important for treatment selection in patients with acute large vessel occlusion (LVO). To assess ischemic core and tissue at risk more accurately, we developed a deep learning model named U-net using computed tomography perfusion (CTP) images. A total of 110 acute ischemic stroke patients undergoing endovascular treatment with major reperfusion ( ≥ 80%) or minimal reperfusion (≤ 20%) were included. Using baseline CTP, we developed two U-net models: one model in major reperfusion group to identify infarct core; the other in minimal reperfusion group to identify tissue at r...
Source: Translational Stroke Research - January 19, 2022 Category: Neurology Source Type: research

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