Bangjun Guo, Mengchun Jiang, Xiang Guo, Chunxiang Tang, Jian Zhong, Mengjie Lu, Chunyu Liu, Xiaolei Zhang, Hongyan Qiao, Fan Zhou, Pengpeng Xu, Yi Xue, Minwen Zheng, Yang Hou, Yining Wang, Jiayin Zhang, Bo Zhang, Daimin Zhang, Lei Xu, Xiuhua Hu, Changsheng Zhou, Jianhua Li, Zhiwen Yang, Xinsheng Mao, Guangming Lu, Longjiang Zhang
Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation...
March 27, 2024: Science Bulletin