Performance assessment of computed tomographic angiog-raphy fractional flow reserve using deep learning:SMART tri-al summary
Wei ZHANG1
You-Bing YIN2
Zhi-Qiang WANG3
Ying-Xin ZHAO3
Dong-Mei SHI3
Yong-He GUO3
Zhi-Ming ZHOU3
Zhi-Jian WANG3
Shi-Wei YANG3
De-An JIA3
Li-Xia YANG3
Yu-Jie ZHOU3
1.Department of Cardiology,Beijing Anzhen Hospital,Capital Medical University,Beijing Institute of Heart Lung and Blood Vessel Disease,Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease,Clinical center for coronary heart disease,Capital Medical University,Beijing,China;The Second Affiliated Hospital of Inner Mon-golia Medical University,Inner Mogolia,010090,China2.Keya Medical Co.,Ltd,Shenzhen,China3.Department of Cardiology,Beijing Anzhen Hospital,Capital Medical University,Beijing Institute of Heart Lung and Blood Vessel Disease,Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease,Clinical center for coronary heart disease,Capital Medical University,Beijing,China
摘要：Background Non-invasive computed tomography angiography(CTA)-based fractional flow reserve(CT-FFR)could become a gatekeeper to invasive coronary angiography.Deep learning(DL)-based CT-FFR has shown promise when compared to invasive FFR.To evaluate the performance of a DL-based CT-FFR technique,DeepVessel FFR(DVFFR).
Methods This retrospective study was designed for iScheMia Assessment based on a Retrospective,single-center Trial of CT-FFR(SMART).Patients suspected of stable coronary artery disease(CAD)and undergoing both CTA and invasive FFR examina-tions were consecutively selected from the Beijing Anzhen Hospital between January 1,2016 to December 30,2018.FFR obtained during invasive coronary angiography was used as the reference standard.DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries.
Results Three hundred and thirty nine patients(60.5±10.0 years and 209 men)and 414 vessels with direct invasive FFR were included in the analysis.At per-vessel level,sensitivity,specificity,accuracy,positive predictive value(PPV)and negative pre-dictive value(NPV)of DVFFR were 94.7％,88.6％,90.8％,82.7％,and 96.7％,respectively.The area under the receiver operating characteristics curve(AUC)was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference(P＜0.0001).At patient level,sensitivity,specificity,accuracy,PPV and NPV of DVFFR were 93.8％,88.0％,90.3％,83.0％,and 95.8％,respectively.The computation for DVFFR was fast with the average time of 22.5±1.9 s.
Conclusions The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effect-ively with improved diagnostic performance over CTA alone.Coronary artery disease(CAD)is a critical disease in which coron-ary artery luminal narrowing may result in myocardial ischemia.Early and effective assessment of myocardial ischemia is essen-tial for optimal treatment planning so as to improve the quality of life and reduce medical costs.
机标关键词：fractionalcomputedlearningsmartflowdeepangiog-raphyassessment
论文发表日期：2025-09-30
在线出版日期：2025-11-14（本平台首次上网日期，不代表文献的发表时间）
页数：9( 793-801 )