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 technolo...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.For Cohort 2,a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed.In Cohort 3,the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated.The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level.Compared with the manually dependent CT-FFR techniques,the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1.This CT-FFR technique has a highly successful(>99%)calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain.Thus,the novel artificial intelligencebased fully automated,on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.展开更多
目的以心脏磁共振(cardiac magnetic resonance,CMR)为金标准,探讨加入人工智能技术的全自动三维超声右心室定量软件(3D Auto RV)评估心脏移植(heart transplantation,HT)术后右心室容积和右心室射血分数(right ventricular ejection fr...目的以心脏磁共振(cardiac magnetic resonance,CMR)为金标准,探讨加入人工智能技术的全自动三维超声右心室定量软件(3D Auto RV)评估心脏移植(heart transplantation,HT)术后右心室容积和右心室射血分数(right ventricular ejection fraction,RVEF)的可行性、准确性及可重复性。方法前瞻性纳入2018年10月至2019年6月于华中科技大学同济医学院附属协和医院行超声心动图复查,并且同意于超声心动图复查后24 h内行CMR检查的HT术后患者46例。分别应用CMR技术、3D Auto RV和常规半自动三维超声右心室量化软件(Tomtec 4D RV function 2.0)获取右心室舒张末期容积(RVEDV)、右心室收缩末期容积(RVESV)、右心室每搏量(RVSV)及RVEF。分别将3D Auto RV、常规半自动Tomtec的测量结果与CMR的测量结果进行比较,比较方法采用配对样本t检验、Pearson相关分析和一致性检验。结果3D Auto RV的可分析率为87%,该软件实现了在27例(59%)患者进行全自动分析,整个分析过程无需调节,分析时间仅需要(12±1)s;另外19例(41%)患者的分析结果需要手动调节,平均分析时间在2 min内,短于常规半自动Tomtec量化软件分析时间[(108±15)s对(160±34)s,P<0.001]。对于右心室容积:3D Auto RV和常规半自动Tomtec分析的RVEDV、RVESV和RVSV,均与CMR分析的相应测量值具有较高的相关性(r=0.77~0.84,均P<0.001)。与CMR测量值比较,3D Auto RV和常规半自动Tomtec技术均低估HT术后患者的RVEDV、RVESV和RVSV,但是3D Auto RV较常规半自动Tomtec的负性偏倚值更小。对于RVEF:3D Auto RV获得的RVEF与CMR获得的RVEF具有很高的相关性与一致性(r=0.84,P<0.001;偏倚值=-1.1%,一致性界限=-8.1%~6.0%)。另外,3D Auto RV手动调节获取的右心室容积和RVEF与CMR相应测量值的相关性(r=0.63~0.72,均P<0.001)低于全自动分析获取的右心室容积和RVEF与CMR相应测量值的相关性(r=0.76~0.82,均P<0.001)。重复性分析显示3D Auto RV获取的RVEDV、RVESV�展开更多
基金supported by the National Key Research and Development Program of China(2022YFC2010004)Jiangsu Province Key Project of Comprehensive Prevention and Control of Chronic Diseases(BE2020699)Top Talent Support Program for young and middle-aged people of Wuxi Health Committee(BJ2023044).
文摘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.For Cohort 2,a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed.In Cohort 3,the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated.The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level.Compared with the manually dependent CT-FFR techniques,the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1.This CT-FFR technique has a highly successful(>99%)calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain.Thus,the novel artificial intelligencebased fully automated,on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.
文摘目的以心脏磁共振(cardiac magnetic resonance,CMR)为金标准,探讨加入人工智能技术的全自动三维超声右心室定量软件(3D Auto RV)评估心脏移植(heart transplantation,HT)术后右心室容积和右心室射血分数(right ventricular ejection fraction,RVEF)的可行性、准确性及可重复性。方法前瞻性纳入2018年10月至2019年6月于华中科技大学同济医学院附属协和医院行超声心动图复查,并且同意于超声心动图复查后24 h内行CMR检查的HT术后患者46例。分别应用CMR技术、3D Auto RV和常规半自动三维超声右心室量化软件(Tomtec 4D RV function 2.0)获取右心室舒张末期容积(RVEDV)、右心室收缩末期容积(RVESV)、右心室每搏量(RVSV)及RVEF。分别将3D Auto RV、常规半自动Tomtec的测量结果与CMR的测量结果进行比较,比较方法采用配对样本t检验、Pearson相关分析和一致性检验。结果3D Auto RV的可分析率为87%,该软件实现了在27例(59%)患者进行全自动分析,整个分析过程无需调节,分析时间仅需要(12±1)s;另外19例(41%)患者的分析结果需要手动调节,平均分析时间在2 min内,短于常规半自动Tomtec量化软件分析时间[(108±15)s对(160±34)s,P<0.001]。对于右心室容积:3D Auto RV和常规半自动Tomtec分析的RVEDV、RVESV和RVSV,均与CMR分析的相应测量值具有较高的相关性(r=0.77~0.84,均P<0.001)。与CMR测量值比较,3D Auto RV和常规半自动Tomtec技术均低估HT术后患者的RVEDV、RVESV和RVSV,但是3D Auto RV较常规半自动Tomtec的负性偏倚值更小。对于RVEF:3D Auto RV获得的RVEF与CMR获得的RVEF具有很高的相关性与一致性(r=0.84,P<0.001;偏倚值=-1.1%,一致性界限=-8.1%~6.0%)。另外,3D Auto RV手动调节获取的右心室容积和RVEF与CMR相应测量值的相关性(r=0.63~0.72,均P<0.001)低于全自动分析获取的右心室容积和RVEF与CMR相应测量值的相关性(r=0.76~0.82,均P<0.001)。重复性分析显示3D Auto RV获取的RVEDV、RVESV�
