Application of deep learning–based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy

  • Sohee Park
  • Sang Min LeeEmail author
  • Namkug KimEmail author
  • Jooae Choe
  • Yongwon Cho
  • Kyung-Hyun Do
  • Joon Beom Seo



To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions.


A CNN system for computer-aided diagnosis on chest radiographs was developed using the full 26-layer You Only Look Once model. A total of 1596 chest radiographs with pneumothorax were used for training. To validate the clinical feasibility of this model, follow-up chest radiographs obtained after PTNB for 1333 pulmonary lesions in 1319 patients in 2016 were prepared as an independent test set. Two experienced radiologists determined the presence of pneumothorax by consensus. The diagnostic performance of the CNN model was assessed using the jackknife free-response receiver operating characteristic method.


The incidence of pneumothorax was 17.9% (247/1379) on 3-h follow-up chest radiographs and 23.3% (309/1329) on 1-day follow-up chest radiographs. Twenty-three (1.7% of all PTNBs) cases required drainage catheter insertion. Our approach had a sensitivity, a specificity, and an area under the curve (AUC), respectively, of 61.1% (151/247), 93.0% (1053/1132), and 0.898 for 3-h follow-up chest radiographs and 63.4% (196/309), 93.5% (954/1020), and 0.905 for 1-day follow-up chest radiographs. The overall accuracy was 87.3% (1204/1379) for 3-h follow-up radiographs and 86.5% (1150/1329) for 1-day follow-up radiographs. The CNN model found all 23 cases of pneumothorax requiring drainage.


Our CNN model had good performance for detection of pneumothorax on chest radiographs after PTNB, especially for those requiring further procedures. It can be used as a screening tool prior to radiologist interpretation.

Key Points

• The CNN model had good performance for detection of pneumothorax on chest radiographs after PTNB and showed high specificity and negative predictive value.

• The CNN model found all cases of pneumothorax requiring drainage after PTNB.

• The CNN model can be used as a screening tool prior to radiologist interpretation.


Machine learning Radiography Lung Biopsy Pneumothorax 



Area under the curve


Convolutional neural network


Jackknife free-response receiver operating characteristic


Percutaneous transthoracic needle biopsy



The authors received funding for this study from the Industrial Strategic Technology Development program (10072064, Development of Novel Artificial Intelligence Technologies to Assist Imaging Diagnosis of Pulmonary, Hepatic, and Cardiac Diseases and Their Integration into Commercial Clinical PACS Platforms) funded by the Ministry of Trade Industry and Energy (MI, Korea).

Compliance with ethical standards


The scientific guarantor of this publication is Sang Min Lee.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution


  1. 1.
    Geraghty PR, Kee ST, McFarlane G, Razavi MK, Sze DY, Dake MD (2003) CT-guided transthoracic needle aspiration biopsy of pulmonary nodules: needle size and pneumothorax rate. Radiology 229(2):475–481CrossRefGoogle Scholar
  2. 2.
    Hiraki T, Mimura H, Gobara H et al (2009) CT fluoroscopy-guided biopsy of 1,000 pulmonary lesions performed with 20-gauge coaxial cutting needles: diagnostic yield and risk factors for diagnostic failure. Chest 136(6):1612–1617CrossRefGoogle Scholar
  3. 3.
    Lee SM, Park CM, Lee KH, Bahn YE, Kim JI, Goo JM (2014) C-arm cone-beam CT-guided percutaneous transthoracic needle biopsy of lung nodules: clinical experience in 1108 patients. Radiology 271(1):291–300CrossRefGoogle Scholar
  4. 4.
    Yeow KM, Su IH, Pan KT et al (2004) Risk factors of pneumothorax and bleeding: multivariate analysis of 660 CT-guided coaxial cutting needle lung biopsies. Chest 126(3):748–754CrossRefGoogle Scholar
  5. 5.
    Geva O, Zimmerman-Moreno G, Lieberman S, Konen E, Greenspan H (2015) Pneumothorax detection in chest radiographs using local and global texture signatures. SPIE Proceedings, Vol. 10575Google Scholar
  6. 6.
    Chan YH, Zeng YZ, Wu HC, Wu MC, Sun HM (2018) Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine. J Healthc Eng 2018:11Google Scholar
  7. 7.
    Cicero M, Bilbily A, Colak E et al (2017) Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol 52(5):281–287CrossRefGoogle Scholar
  8. 8.
    Blumenfeld A, Konen E, Greenspan H (2018) Pneumothorax detection in chest radiographs using convolutional neural networks. SPIE Proceedings, Vol. 10575Google Scholar
  9. 9.
    Redmon J, Farhadi A (2016) YOLO9000: better, faster, stronger. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517–6525Google Scholar
  10. 10.
    Park SH, Han K (2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286(3):800–809CrossRefGoogle Scholar
  11. 11.
    Prevedello LM, Erdal BS, Ryu JL et al (2017) Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 285(3):923–931CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology and Research Institute of Radiology, Asan Medical Center, College of MedicineUniversity of UlsanSeoulSouth Korea
  2. 2.Department of Convergence Medicine, Asan Medical Center, College of MedicineUniversity of UlsanSeoulSouth Korea

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