A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images [0.03%]
基于二维冠脉造影图像的冠状动脉钙化病变多模态深度学习检测方法
Edoardo De Rose,Ciro Benito Raggio,Ahmad Riccardo Rasheed et al.
Edoardo De Rose et al.
Purpose: Identifying and quantifying coronary artery calcification (CAC) is crucial for preoperative planning, as it helps to estimate both the complexity of the 2D coronary angiography (2DCA) procedure and the risk of de...
Hierarchical Auto-labeling of Coronary Arteries on CT Coronary Angiography Images [0.03%]
CT冠状动脉造影图像中冠脉的分层自动标注方法研究
Jiangyun Li,Zhongkang Lu,Shuang Leng et al.
Jiangyun Li et al.
The auto-labeling of coronary artery segments plays an important role in the diagnosis of cardiovascular diseases. Due to the high degree of complexity and diversity in coronary artery structures, it is still a very challenging task after m...
Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation [0.03%]
基于先进人工智能的定量冠状动脉造影评估及其与手动QCA和视觉估计对比的研究
Jihye Chae,Jihoon Kweon,Gyung-Min Park et al.
Jihye Chae et al.
This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard.
Online tree-structure-constrained RPCA for background subtraction of X-ray coronary angiography images [0.03%]
基于在线树结构约束RPCA的X光冠状动脉造影图像背景减除方法
Saeid Shakeri,Farshad Almasganj
Saeid Shakeri
Background and objective: Background subtraction of X-ray coronary angiograms (XCA) can significantly improve the diagnosis and treatment of coronary vessel diseases. The XCA background is complex and dynamic due to struc...
Integrated deep learning model for automatic detection and classification of stenosis in coronary angiography [0.03%]
基于深度学习的冠状动脉造影狭窄检测与分类模型研究
Tao Wang,Xiaojun Su,Yuchao Liang et al.
Tao Wang et al.
The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection.
Computing pulsatile blood flow of coronary artery under incomplete boundary conditions [0.03%]
冠状动脉不完全边界条件下脉动血流的计算方法研究
WenJun Pu,Yan Chen,Shuai Zhao et al.
WenJun Pu et al.
Computational fluid dynamics (CFD) methods integrating coronary angiography images and fractional flow reserve (FFR) offer a novel approach for computing mean coronary blood flow.
Effective descriptor extraction strategies for correspondence matching in coronary angiography images [0.03%]
冠状动脉造影图像配准中有效的描述子提取策略
Hyun-Woo Kim,Soon-Cheol Noh,Sun-Hwa Kim et al.
Hyun-Woo Kim et al.
The importance of 3D reconstruction of coronary arteries using multiple coronary angiography (CAG) images has been increasingly recognized in the field of cardiovascular disease management. This process relies on the camera matrix's optimiz...
The role of syntax score on survival in young acute coronary syndrome patients [0.03%]
年轻急性冠脉综合征患者中语法分数对生存率的影响
M Bilgin,R Dokuyucu
M Bilgin
SS was calculated from the coronary angiography images of the patients.
Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images [0.03%]
基于血管内造影图像估算冠脉血流储备分数的无创方法
Hadis Edrisnia,Mohammad Hossein Sarkhosh,Bahram Mohebbi et al.
Hadis Edrisnia et al.
This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet
Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning [0.03%]
基于人工智能的主血管定量冠状动脉造影的深度学习技术
Young In Kim,Jae-Hyung Roh,Jihoon Kweon et al.
Young In Kim et al.
A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts.
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