AIMC Topic: Thrombectomy

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Workflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.

Journal of neurointerventional surgery
BACKGROUND: Automated machine learning (ML)-based large vessel occlusion (LVO) detection algorithms have been shown to improve in-hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction a...

Impact of imaging biomarkers from body composition analysis on outcome of endovascularly treated acute ischemic stroke patients.

Journal of neurointerventional surgery
BACKGROUND: We investigate the association of imaging biomarkers extracted from fully automated body composition analysis (BCA) of computed tomography (CT) angiography images of endovascularly treated acute ischemic stroke (AIS) patients regarding an...

Development and internal validation of multimodal machine learning models for predicting eligibility for mechanical thrombectomy in suspected stroke patients using routinely collected clinical and imaging data.

PloS one
BACKGROUND: Mechanical thrombectomy (MT) eligibility for acute ischemic stroke (AIS) patients depends upon clinical and advanced imaging assessments like CT perfusion (CTP). Assessment complexities and limited access to advanced imaging investigation...

Design and optimization of an automatic deep learning-based cerebral reperfusion scoring (TICI) using thrombus localization.

Journal of neuroradiology = Journal de neuroradiologie
BACKGROUND: The Thrombolysis in Cerebral Infarction (TICI) scale is widely used to assess angiographic outcomes of mechanical thrombectomy despite significant variability. Our objective was to create and optimize an artificial intelligence (AI)-based...

Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy.

Scientific reports
This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict hemo...

Benchmarking reinforcement learning algorithms for autonomous mechanical thrombectomy.

International journal of computer assisted radiology and surgery
PURPOSE: Mechanical thrombectomy (MT) is the gold standard for treating acute ischemic stroke. However, challenges such as operator radiation exposure, reliance on operator experience, and limited treatment access remain. Although autonomous robotics...

You Cannot Manage What You Do Not Measure: Advances in Global Stroke Interventions and the Role of the Mechanical Thrombectomy Access Score.

Cardiology in review
Global disparities in stroke care, particularly in acute interventions like mechanical thrombectomy (MT), remain profound, with the Mechanical Thombectomy Global Access for Stroke study reporting a median global MT access of just 2.79%. Furthermore, ...

Reinforcement learning for safe autonomous two-device navigation of cerebral vessels in mechanical thrombectomy.

International journal of computer assisted radiology and surgery
PURPOSE: Autonomous systems in mechanical thrombectomy (MT) hold promise for reducing procedure times, minimizing radiation exposure, and enhancing patient safety. However, current reinforcement learning (RL) methods only reach the carotid arteries, ...

Rapid Blood Clot Removal via Remote Delamination and Magnetization of Clot Debris.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Micro/nano-scale robotic devices are emerging as a cutting-edge approach for precision intravascular therapies, offering the potential for highly targeted drug delivery. While employing micro/nanorobotics for stroke treatment is a promising strategy ...

Deep Learning-Assisted Diagnosis of Malignant Cerebral Edema Following Endovascular Thrombectomy.

Academic radiology
BACKGROUND: Malignant cerebral edema (MCE) is a significant complication following endovascular thrombectomy (EVT) in the treatment of acute ischemic stroke. This study aimed to develop and validate a deep learning-assisted diagnosis model based on t...