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Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after
Sohee Park1, Sang Min Lee2, Namkug Kim3
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138736, South Korea.
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.
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