上海交通大学学报（医学版）

上海交通大学学报（医学版） >

2021 , Vol. 41 >Issue 7: 920 - 925

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.07.012

论著 · 临床研究

人工智能在胸部创伤肋骨骨折CT诊断中应用的初步研究

  • 刘想 ,
  • 谢辉辉 ,
  • 许玉峰 ,
  • 陶晓峰 ,
  • 柳林 ,
  • 吴迪嘉 ,
  • 王霄英
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  • 1.北京大学第一医院医学影像科，北京 100032
    2.上海交通大学医学院附属第九人民医院影像科，上海 200011
    3.吉林大学中日联谊医院影像科，长春 130031
    4.上海联影智能医疗科技有限公司，上海 201800
刘想（1995—），女，博士生；电子信箱：lx_mango@163.com。

网络出版日期: 2021-08-03

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Application of artificial intelligence to CT diagnosis of thoracic traumatic rib sites: a preliminary study

  • Xiang LIU ,
  • Hui-hui XIE ,
  • Yu-feng XU ,
  • Xiao-feng TAO ,
  • Lin LIU ,
  • Di-jia WU ,
  • Xiao-ying WANG
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  • 1.Department of Radiology, Peking University First Hospital, Beijing 100032, China
    2.Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
    3.Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun 130031, China
    4.Shanghai United Imaging Intelligence Co. , Ltd. , Shanghai 201800, China

Online published: 2021-08-03

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摘要

目的·评价人工智能（artificial intelligence，AI）软件在胸部电子计算机体层扫描（computed tomograph，CT）图像上自动检测创伤性肋骨骨折的诊断效能。方法·收集2019年8月—9月因急性胸部外伤而行CT扫描的393例连续数据。以3位资深影像专家的共同阅片结果作为评估AI检出肋骨骨折病灶的参考标准。在病灶层面计算全部肋骨外伤改变以及不同类型肋骨骨折（错位型骨折、轻微骨折、骨皮质扭曲）的敏感度。并分别以全部肋骨外伤改变和错位型肋骨骨折为研究对象，在患者和肋骨2个层面分析AI自动检出肋骨骨折的效能。结果·在病灶层面上，AI检出全部肋骨外伤改变的敏感度为81.75%，错位型肋骨骨折检出的敏感度为94.85%，在3种骨折类型中最高（P=0.000）。以全部肋骨外伤改变为研究对象，AI在肋骨层面的骨折检出敏感度、特异度、阳性预测值、阴性预测值分别为82.45%、98.33%、75.30%和98.91%；在患者层面分别为90.91%、76.21%、77.63%和90.23%。以错位型肋骨骨折为研究对象时，AI在肋骨层面的骨折检出敏感度、特异度、阳性预测值、阴性预测值分别为94.57%、98.26%、51.94%和99.89%；在患者层面分别为95.56%、74.59%、52.76%和98.26%。结论·AI软件检出肋骨骨折病灶具有较高的敏感度，可望用于急性胸部外伤CT读片的初筛和风险分层。

关键词： 肋骨骨折; CT成像; 人工智能; 急诊影像学

本文引用格式

刘想 , 谢辉辉 , 许玉峰 , 陶晓峰 , 柳林 , 吴迪嘉 , 王霄英 . 人工智能在胸部创伤肋骨骨折CT诊断中应用的初步研究[J]. 上海交通大学学报（医学版）, 2021 , 41(7) : 920 -925 . DOI: 10.3969/j.issn.1674-8115.2021.07.012

Abstract

Objective

·To assess the detection rate of an artificial intelligence (AI) software for acute traumatic rib fractures on chest computed tomograph (CT) images.

Methods

·A consecutive cohort of CT images of the patients with acute chest trauma were collected from August 2019 to September 2019 (n=393). The reference standard was defined as the consensus reading results of three radiologist experts. At the lesion level, the sensitivity was studied, including all lesions and different types of rib fractures (i.e., displaced rib fractures, mild fractures, and cortical distortion). The sensitivity was also studied at both patient and rib levels.

Results

·At the lesion level, the total rib fracture detection sensitivity of AI was 81.75%. For displaced rib fractures, the sensitivity was 94.85%, which was the highest among the three types of fractures (P=0.000). When all types of rib fractures were taken as the object of study, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of AI were 82.45%, 98.33%, 75.30% and 98.91% respectively at the rib level, and 90.91%, 76.21%, 77.63% and 90.23% respectively at the patient level. When displaced rib fracture were taken as the object, the sensitivity, specificity, PPV and NPV of AI were 94.57%, 98.26%, 51.94% and 99.89% respectively at the rib level, and 95.56%, 74.59%, 52.76% and 98.26% at the patient level.

Conclusion

·AI software has high sensitivity in detecting the fracture sites, and it could be potentially used for screening the thorax CT images in patients with acute chest trauma.

Key words： rib fracture; computed tomography; artificial intelligence; emergency radiology

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