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Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs

 Hwiyoung Kim  ;  Kye Ho Lee  ;  Kyunghwa Han  ;  Ji Won Lee  ;  Jin Young Kim  ;  Dong Jin Im  ;  Yoo Jin Hong  ;  Byoung Wook Choi  ;  Jin Hur 
 JAMA NETWORK OPEN, Vol.6(1) : e2253820, 2023-01 
Journal Title
Issue Date
Deep Learning* ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Radiographic Image Interpretation, Computer-Assisted / methods ; Radiography ; Radiography, Thoracic / methods
Importance: Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures. Objectives: To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs. Design, Setting, and Participants: This decision analytical modeling study used data from 3 centers between November 2015 and July 2019 from 1449 patients. The DLBS nodule-detection algorithm was trained using single-center data (institute 1) of 998 chest radiographs. The DLBS algorithm was validated using 2 external data sets (institute 2, 246 patients; and institute 3, 205 patients). Statistical analysis was performed from March to December 2021. Exposures: DLBS nodule-detection algorithm. Main Outcomes and Measures: The nodule-detection performance of DLBS model was compared with the convolution neural network nodule-detection algorithm (original model). Reader performance testing was conducted by 3 thoracic radiologists assisted by the DLBS algorithm or not. Sensitivity and false-positive markings per image (FPPI) were compared. Results: Training data consisted of 998 patients (539 men [54.0%]; mean [SD] age, 54.2 [9.82] years), and 2 external validation data sets consisted of 246 patients (133 men [54.1%]; mean [SD] age, 55.3 [8.7] years) and 205 patients (105 men [51.2%]; mean [SD] age, 51.8 [9.1] years). Using the external validation data set of institute 2, the bone-suppressed model showed higher sensitivity compared with that of the original model for nodule detection (91.5% [109 of 119] vs 79.8% [95 of 119]; P < .001). The overall mean of FPPI with the bone-suppressed model was reduced compared with the original model (0.07 [17 of 246] vs 0.09 [23 of 246]; P < .001). For the observer performance testing with the data of institute 3, the mean sensitivity of 3 radiologists was 77.5% (95% [CI], 69.9%-85.2%), whereas that of radiologists assisted by DLBS modeling was 92.1% (95% CI, 86.3%-97.3%; P < .001). The 3 radiologists had a reduced number of FPPI when assisted by the DLBS model (0.071 [95% CI, 0.041-0.111] vs 0.151 [95% CI, 0.111-0.210]; P < .001). Conclusions and Relevance: This decision analytical modeling study found that the DLBS model was more sensitive to detecting pulmonary nodules on chest radiographs compared with the original model. These findings suggest that the DLBS model could be beneficial to radiologists in the detection of lung nodules in chest radiographs without need of the specialized equipment or increase of radiation dose.
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1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hwiyoung(김휘영)
Lee, Kye Ho(이계호) ORCID logo https://orcid.org/0000-0001-5568-1833
Im, Dong Jin(임동진) ORCID logo https://orcid.org/0000-0001-8139-5646
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
Han, Kyung Hwa(한경화)
Hur, Jin(허진) ORCID logo https://orcid.org/0000-0002-8651-6571
Hong, Yoo Jin(홍유진) ORCID logo https://orcid.org/0000-0002-7276-0944
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