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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
Chest
  • 138 Downloads

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Machine learning Radiography Lung Biopsy Pneumothorax 

Abbreviations

AUC

Area under the curve

CNN

Convolutional neural network

JAFROC

Jackknife free-response receiver operating characteristic

PTNB

Percutaneous transthoracic needle biopsy

Notes

Funding

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

Guarantor

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.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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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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