摘要
目的 验证基于CT肺血管成像(computed tomography pulmonary angiography, CTPA)的肺栓塞人工智能识别系统(PE-AI)的诊断效能及危险分层能力,分析其在实际临床工作中的诊断价值。方法 收集我院2023年1月1日至2023年10月10日疑似PE患者行CTPA检查病例416例。采用双盲法由2名低年资影像医师与PE-AI分别对收集病例进行栓子检出和诊断,并分别记录诊断时间;以3名高年资影像医师结合临床随访结局作为本研究的金标准,评价AI、人工与PE-AI的诊断效能,并绘制受试者操作曲线(receiver operating characteristic curve, ROC),使用Delong-t检验进行比较。肺栓塞确诊病例分别收集AI及人工计算的肺栓塞指数(pulmonary artery obstruction index, PAOI),并进行一致性分析。结果 PE-AI、人工及联合诊断的曲线下面积AUC(area under curve, AUC)分别为85.6%、90.8%和95.1%,三组AUC之间差异具有统计学意义(P<0.05);PE-AI的读片时间[(0.16±0.07)min]明显低于人工[(4.42±1.85)min,P<0.001]及联合诊断[(4.58±1.84)min,P<0.001]。肺动脉栓塞确诊病例亚组分析中,PE-AI与人工测得的PAOI具有较高的一致性(intraclass correlation efficient, ICC=0.80)。结论 AI可以在短时间内快速识别肺动脉栓子,辅助放射科医师提高诊断效率;同时通过对肺动脉PAOI的智能检测,有助于肺栓塞患者的危险分层,优化诊疗流程。
Objective To validate the diagnostic performance and risk stratification ability of an AI-based recognition system(PE-AI)for pulmonary embolism(PE)using computed tomography pulmonary angiography(CTPA)so as to analyze its diagnostic value in clinical practice.Methods A total of 416 patients with suspected PE who underwent CTPA from January 1,2023 to December 10,2023 at our hospital were included in this study.Two junior radiologists and PE-AI separately detected and diagnosed emboli in the collected cases by double-blind method,and recorded the diagnosis time respectively.Three senior radiologists reviewing with clinical follow-up results were used as the gold standard in this study.Diagnostic performance was evaluated by using the receiver operating characteristic(ROC)curve analysis and Delong-t test.For positive cases,the pulmonary artery obstruction index(PAOI)calculated by AI and manually were collected respectively and consistency analysis was performed.Results The area under the curve(AUC)of PE-AI,manual and combined diagnosis was 85.6%,90.8%and 95.1%,respectively,which differed significantly(P<0.05).The reading time of PE-AI[(0.16±0.07)min]was significantly lower than the time of manual[(4.42±1.85)min,P<0.001]and combined diagnosis[(4.58±1.84)min,P<0.001].The PAOI measured by PE-AI and manually had high consistency(intraclass correlation efficient,ICC=0.80)in the subgroup analysis of confirmed cases.Conclusion AI can quickly identify pulmonary artery emboli in a short time and assist radiologists to improve diagnostic efficiency.At the same time,through the intelligent detection of PAOI,it is helpful for the risk stratification of patients with PE and optimizing the diagnosis and treatment pathway for pulmonary embolism.
作者
杨舒同
李竹君
金超
侯伟
赵文哲
张宝平
田倩
肖瑶
荐志洁
刘哲
YANG Shutong;LI Zhujun;JIN Chao;HOU Wei;ZHAO Wenzhe;ZHANG Baoping;TIAN Qian;XIAO Yao;JIAN Zhijie;LIU Zhe(Department of Radiology,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061;Department of Dermatology,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,China)
出处
《西安交通大学学报(医学版)》
北大核心
2025年第1期157-161,共5页
Journal of Xi’an Jiaotong University(Medical Sciences)
基金
国家自然科学基金项目(No.12226007)。
关键词
肺动脉栓塞
CT肺血管成像
人工智能
肺栓塞指数(PAOI)
pulmonary artery embolism
CT pulmonary angiography(CTPA)
artificial intelligence(AI)
pulmonary artery obstruction index(PAOI)
作者简介
通信作者:刘哲.E-mail:imagingliuzhe@163.com。
