AIMC Topic: Thrombectomy
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DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.
European radiology
Feb 27, 2023
OBJECTIVES: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a mode...
Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jan 16, 2023
OBJECTIVE: Prediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO.
Modified left robot-assisted radical nephrectomy combined with level I inferior vena cava thrombectomy: A case report and a lesson.
Asian journal of surgery
Dec 14, 2022
Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke.
Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
Nov 7, 2022
PURPOSE: Outcome prediction of large vessel occlusion of the anterior circulation in patients with wake-up stroke is important to identify patients that will benefit from thrombectomy. Currently, mismatch concepts that require MRI or CT-Perfusion (CT...
Preliminary experience of oblique occlusion technique in robot-assisted infrahepatic inferior vena cava thrombectomy: step-by-step procedures and short term outcomes.
BMC surgery
Nov 4, 2022
BACKGROUND: We aimed to compare the oncological outcomes between the oblique occlusion technique and the traditional technique for robot-assisted radical nephrectomy (RARN) with inferior vena cava (IVC) thrombectomy, and to explore the safety and eff...
Deep learning-based classification of DSA image sequences of patients with acute ischemic stroke.
International journal of computer assisted radiology and surgery
May 23, 2022
PURPOSE: Recently, a large number of patients with acute ischemic stroke benefited from the use of thrombectomy, a minimally invasive intervention technique for mechanically removing thrombi from the cerebrovasculature. During thrombectomy, 2D digita...
Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support.
Cerebrovascular diseases extra
Feb 8, 2022
BACKGROUND: Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis is used for faster and standardized patient selection. However, there is little information on how s...
Spatio-temporal deep learning for automatic detection of intracranial vessel perforation in digital subtraction angiography during endovascular thrombectomy.
Medical image analysis
Jan 29, 2022
Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to de...
Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.
Stroke
Sep 9, 2021
BACKGROUND AND PURPOSE: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspe...
A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Apr 19, 2021
INTRODUCTION: Novel machine learning (ML) methods are being investigated across medicine for their predictive capabilities while boasting increased adaptability and generalizability. In our study, we compare logistic regression with machine learning ...
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