后疫情时代人工智能肺炎辅助诊断系统的临床应用场景探索  

Exploring the application scenario of artificial intelligence-assisted diagnostic system for pneumonia in the post-pandemic era

作　　者：陈冲 王大为 于朋鑫 周文 孙希子 唐媛媛 赵赟 刘秋雨 谢开 周舒畅 李大胜[3] 赵绍宏[4] 夏黎明[1] CHEN Chong;WANG Da-wei;YU Peng-xin(Department of Radiology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China)

机构地区：[1]华中科技大学同济医学院附属同济医院放射科,武汉430030 [2]推想医疗科技股份有限公司先进研究院,北京100025 [3]北京市海淀医院北京大学第三医院海淀院区放射科,北京100080 [4]中国人民解放军总医院放射科,北京100853

出　　处：《放射学实践》2024年第7期888-894,共7页Radiologic Practice

基　　金：科技创新2030——“新一代人工智能”重大项目(2021ZD0111104)。

摘　　要：目的:基于临床验证性研究,探索后疫情时代人工智能肺炎辅助诊断系统(AI-ADS)潜在的临床应用场景。方法:回顾性收集了来自三家医院的1049例胸部CT扫描数据,包括400例胸部CT表现正常的病例、233例新冠肺炎病例和416例其他社区获得性肺炎病例。六名高年资放射科医师参与了数据标注工作。采用敏感度、特异度、Dice系数和受试者操作特征(ROC)曲线下面积(AUC)评估人工智能系统在相应场景中的性能表现。结果:AI-ADS基于胸部CT识别各类型肺炎、细菌性肺炎、新冠肺炎、其他病毒性肺炎和其他社区获得性肺炎的AUC分别为0.968、0.983、0.992、0.941、0.958,检测各种肺炎的敏感度均超过0.90;鉴别病毒性肺炎和非病毒性肺炎的AUC达到0.950,敏感度为0.885,特异度为0.910;在新冠肺炎和其他社区获得性肺炎测试集中分割肺炎区域的平均Dice系数分别达到0.851和0.753。结论:AI-ADS在肺炎的检测预警、病灶定量分析以及鉴别诊断方面具有良好的性能,具备了后疫情时代的多场景应用价值。Objective:To investigate the potential clinical practice of artificial intelligence(AI)-assisted diagnostic system(AI-ADS) for pneumonia in the post-pandemic era by exploring various application scenarios in different patient cohorts.Methods:The study retrospectively collected 1049 sets of chest CT scans from patients either diagnosed as normal(n=400),COVID-19(n=233),or other community-acquired pneumonia(CAP)(n=416) at three hospitals.We explored the potential clinical practice by validating its performance in the detection,classification,and lesion measurement(segmentation) of different types of pneumonia.Six senior radiologists participated in the establishment of the gold standard for lesion labeling and segmentation.Sensitivity,specificity,Dice coefficient,and area under the receiver operating characteristic curve(AUC) were utilized to evaluate the performance.Results:AI-ADS displayed decent detection performance on different types of pneumonia,as evidenced by the AUC of 0.968,983,0.992,0.941,and 0.958 for overall types,bacterial,COVID-19,non-COVID viral,and other pneumonia,respectively.The detection sensitivity all reached above 0.9.Additionally,the system differentiated viral and non-viral pneumonia with an AUC of 0.950,a sensitivity of 0.885,and a specificity of 0.910.Of note,AI-ADS achieved good segmentation results on both COVID-19 cases(internal test set,averaged DICE=0.851) and non-COVID cases(external test set,averaged DICE=0.753).Conclusion:With performance improvement,AI-ADS can detect various types of pneumonia and differentiate viral pneumonia from others.It shows a decent lesion segmentation capacity among different types of pneumonia,indicating its potential clinical application in the post-pandemic era.

关 键 词：新型冠状病毒感染 社区获得性肺炎 体层摄影术 X线计算机 人工智能 辅助诊断系统 

分 类 号：R814.42[医药卫生—影像医学与核医学] R563.1[医药卫生—放射医学] R-05[医药卫生—临床医学]