BACKGROUND: To evaluate the effectiveness of machine learning (ML) techniques in predicting negative remodeling in uncomplicated Stanford type B intramural hematoma (IMHB) during the acute phase.
Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
Mar 16, 2023
PURPOSE: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD) using computed tomography angiogra...
Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of t...
Arteriosclerosis, thrombosis, and vascular biology
Aug 12, 2021
Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that inc...
Journal of the American Heart Association
May 17, 2021
Background An artificial intelligence vessel segmentation tool, Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE), was used to analyze a large databank of popliteal arteries imaged through the OAI (Osteoarthritis ...
OBJECTIVES: This study aimed to construct a risk prediction model for distal aortic enlargement in patients with type B aortic dissection (TBAD) treated with proximal thoracic endovascular aortic repair (TEVAR).
The Journal of thoracic and cardiovascular surgery
Dec 22, 2018
OBJECTIVES: Our objective was to examine the role of the provisional extension to induce complete attachment (PETTICOAT) aortic dissection repair technique with bare metal stents (BMSs) in abdominal remodeling of residual DeBakey type I aortic dissec...
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