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 inde...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?展开更多
基金National Key Research and Development Program of China(No.2019YFE0107800)the Beijing Municipal Science and Technology Commission(No.Z201100005620009)。
文摘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?
