Performance evaluation of deep learning-based post-processing and diagnostic reporting system for coronary CT angiography: a clinical comparative study  

作　　者：Nan Luo Yi He Jitao Fan Ning Guo Guang Yang Yuanyuan Kong Jianyong Wei Tao Bi Jie Zhou Jiaxin Cao Xianjun Han Fang Li Shiyu Zhang Rujing Sun Zhaozhao Wang Tian Ma Lixue Xu Hui Chen Hongwei Li Zhenchang Wang Zhenghan Yang 

机构地区：[1]Department of Radiology,Beijing Friendship Hospital of Capital Medical University,Bejing 100050,China [2]Shukun(Bejing)Technology Company Ltd.,Bejing 100102,China [3]Statistics Section,ejing Friendship Hospital of Capital Medical Uuniversity,Bejing 100050,China [4]Department of Radilogy,Bejing Anzhen Hospital of Capital Medical University,Bejing 100011,China [5]Department of Radiology,Bejing Chest Hospital of Capital Medical University,Beijing 100010,China [6]Department of Cardiology,Bejing Friendship Hospital of Capital Medical University,Beijing 100050,China

出　　处：《Chinese Medical Journal》2022年第19期2366-2368,共3页中华医学杂志（英文版）

基　　金：National Key Research and Development Program of China(No.2019YFE0107800);the Beijing Municipal Science and Technology Commission(No.Z201100005620009)。

摘　　要：To the Editor:Coronary computed tomography angiography(CCTA)has been increasingly,widely performed for diagnosing coronary artery,disease,lAnatomical diagnosis,that is,stenosis grading,is stillthe main diagnostic index provided'by most CCTA tests.Post-processing and interpretation of stenosis are 2 essential'steps that need to be performed bycardiovascular imaging professionals from scan completion to diagnosis conclusion,which is repetitive and time-consuming,taking an average of 30 minutes each case in China and becoming the bottleneck and gradually creating an imbalance between supply and demand.In ine with the rapid development of artificial intelligence(Al)technology in recent years,it has been expected to solve these specific problems.We developed an AI system for automating post-processing and diagnostic reporting of CCTA data using deep learning algorithms to establishanew1-clickworkflowforeverydayuse,namely,CCTA-AI(Figure 1).To further assess its capabilities,this study intends to answer 2 following questions:To what extent can it improve the efficiency of post-processing?To what extent can CCTA-AI detect and calculate coronary artery stenosis due to each atherosclerotic plaque?

关 键 词：CORONARY diagnosis FIGURE 

分 类 号：R816.2[医药卫生—放射医学]