Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution

Zhuang, Zixu; Si, Lin; Wang, Sheng; Xuan, Kai; Ouyang, Xin; Zhan, Yiqiang; Xue, Zhong; Zhang, Lichi; Shen, Dinggang; Yao, Weiwu; Wang, Qian

doi:10.1109/tmi.2022.3206042

Cited by 6 publications

(1 citation statement)

References 57 publications

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“…Graphs offer powerful tools for representing such irregular structures [20], making them well-suited for depicting the explicit geometry of the coronary artery. In contrast to traditional CNNs tailored for grid-like data such as images, Graph Neural Networks (GNNs) excel at extracting valuable insights and features from non-grid, irregular, and interconnected graph representations, which makes them especially proficient in capturing detailed geometric information [21][22][23][24]. However, none of these existing approaches considered explicitly the geometric information for future events prediction.…”

Section: Introductionmentioning

confidence: 99%

Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective

Sun,

Belmpas,

Senouf

et al. 2024

Preprint

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Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction at both lesion and patient level from invasive coronary angiography. AngioGraphCAD is evaluated across two clinical cohorts at the lesion level and one datatset at the patient level, achieving superior performance compared to clinical measures. This is the first study that highlights the importance of geometry information in advancing future events prediction from invasive coronary angiography. Given the significance of the clinical question and the innovative nature of the proposed methodology, this work could pave the way for the development of an AI framework fueled by patient-specific data in cardiology, potentially revolutionizing personalized decision-making in managing coronary artery diseases for individual patients.

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