摘要
目的以有创冠状动脉造影(ICA)为参考标准,探讨人工智能(AI)辅助的冠状动脉CT血管成像(CCTA)诊断阻塞性冠状动脉狭窄的效能。方法回顾性收集行CCTA检查并于3个月内行ICA检查的50例疑患冠状动脉疾病(CAD)的病人,男34例,女16例,平均年龄(61.8±8.5)岁。AI软件、不同年资医师(低/中/高年资)及AI+不同年资医师分别对入组病人CCTA影像进行后处理并解读。将ICA和CCTA上冠状动脉管腔狭窄≥50%定义为阻塞性冠状动脉狭窄。采用Agatston积分法测量病人的钙化积分值,并将病人分为低钙化组(钙化积分<100)和高钙化组(钙化积分≥100)。采用独立样本t检验对AI、医师及AI+医师的图像后处理和解读时间进行两两比较。以ICA为参考标准,分析AI在不同研究水平和高/低钙化组的诊断价值,并比较AI、不同年资医师和AI+不同年资医师的诊断敏感度、特异度、阳性预测值、阴性预测值、准确度及受试者操作特征(ROC)曲线下面积(AUC)。采用Pearson卡方检验或Fisher精确概率检验比较组间差异,采用DeLong检验比较AUC。结果50例病人共分析195支血管424个节段。AI和AI+医师的平均后处理和解读时间均低于单独医师诊断的时间(均P<0.05),AI的时间较低/中/高年资医师分别减少了80%、76.8%和75%;AI+低/中/高年资医师较单独医师分别减少了67%、64%、57.9%。在病人、血管及节段水平,AI诊断阻塞性冠状动脉狭窄的敏感度分别为93.7%、83.1%、67.7%,特异度为50.0%、89.0%、91.0%,准确度为92%、86.7%、85.6%,阳性预测值为97.8%、83.1%、69.8%,阴性预测值为25%、89.0%、90.2%,AUC为0.87、0.89、0.83;在血管及节段水平,AI对低钙化组的特异度高于高钙化组(均P<0.05)。在血管水平,AI诊断的AUC值均低于中/高年资医师(均P<0.05);其余研究水平,AI与其他不同年资医师诊断的AUC值差异均无统计学意义(均P>0.05)。3种研究水平下,AI+低/中/高年资医师诊断的AUC�
Objective To investigate the diagnostic performance of artificial intelligence(AI)based coronary CT angiography(CCTA)in detecting obstructive coronary artery stenosis with invasive coronary angiography(ICA)as reference standard.Methods A retrospective analysis was performed on 50 patients with suspected coronary artery disease(CAD)who underwent CCTA examination and ICA examination within 3-month interval,including 34 males and 16 females,with an average age of(61.8±8.5)years.AI software,cardiovascular radiologists with different experiences(low/intermediate/high experience),and AI+radiologists with different experiences independently performed image post-processing and interpretation of CCTA.Luminal stenosis≥50%was defined as obstructive coronary artery stenosis in both ICA and CCTA.The calcium scores on a per-patient were measured by Agatston integral method,they were divided into two groups:low calcification group(calcium scores<100)and high calcification group(calcium scores≥100).Independent sample t test was used to compare the difference in post-processing time between AI and radiologists,and between AI+radiologists and radiologists.The diagnostic sensitivity,specificity,positive predictive value,negative predictive value,accuracy,and area under the reciever operating characteristic curve(AUC)of AI were calculated for low/high calcification groups and low/intermediate/high experienced doctors with ICA as reference standard.The diagnostic performances were further compared among the AI,radiologists,and AI+radiologists.The Pearson chi-square test,Fisher’s exact test,and DeLong test were used to compare the AUC differences in performances between groups when appropriate.Results A total of 195 vessels and 424 segments were analyzed in 50 patients.The average post-processing and interpretation times of AI and AI+radiologists were shorter than that of independent radiologists(all P<0.05).The average post-processing and interpretation times of AI was reduced by 80%,76.8%,and 75%compared with low/intermediate
作者
刘春雨
谢媛
苏晓芹
杨振悦
陈随
周长圣
李建华
徐峰
LIU Chunyu;XIE Yuan;SU Xiaoqin;YANG Zhenyue;CHEN Sui;ZHOU Changsheng;LI Jianhua;XU Feng(Department of Diagnostic Radiology,Jinling Hospital,Medical School of Nanjing University,General Hospital of Eastern Theater Command,Nanjing 210002,China;Department of Cardiology,Jinling Hospital,Medical School of Nanjing University,General Hospital of Eastern Theater Command,Nanjing 210002,China;Department of Radiology,The Affiliated Suqian First People’s Hospital of Nanjing Medical University)
出处
《国际医学放射学杂志》
北大核心
2021年第5期516-522,共7页
International Journal of Medical Radiology
基金
国家重点研发计划项目(2017YFC0113400)。
关键词
人工智能
诊断效能
冠状动脉CT血管成像
冠状动脉狭窄
Artificial intelligence
Diagnostic performance
Coronary computed tomography angiography
Coronary artery stenosis
