JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE) ›› 2021, Vol. 41 ›› Issue (7): 920-925.doi: 10.3969/j.issn.1674-8115.2021.07.012

• Clinical research • Previous Articles    

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

Xiang LIU1(), Hui-hui XIE1, Yu-feng XU1, Xiao-feng TAO2, Lin LIU3, Di-jia WU4, Xiao-ying WANG1()   

  1. 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:2021-07-28 Published:2021-08-03
  • Contact: Xiao-ying WANG;;

Abstract: Objective

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


·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.


·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.


·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

CLC Number: